Blood and cell analysis using an imaging flow cytometer

ABSTRACT

Multimodal or multispectral images of cells comprising a population of cells are simultaneously collected. Photometric and/or morphometric image features identifiable in the images are used to identify differences between first and second populations of cells. The differences can include changes in a relative percentage of different cell types in each population, or a change in a first type of cell present in the first population of cells and the same type of cell in the second population of cells. The changes may be indicative of a disease state, indicative of a relative effectiveness of a therapy, or indicative of a health of the person from whom the cells populations were obtained.

RELATED APPLICATIONS

This application is a continuation in part application based on priorcopending patent application Ser. No. 12/362,170, filed on Jan. 29,2009, which itself is a divisional application based on prior patentapplication Ser. No. 11/344,941, filed on Feb. 1, 2006, now U.S. Pat.No. 7,522,758, the benefit of the filing date of which is hereby claimedunder 35 U.S.C. §120. Patent application Ser. No. 11/344,941 is based ona prior provisional application Ser. No. 60/649,373, filed on Feb. 1,2005, the benefit of the filing date of which is hereby claimed under 35U.S.C. §119(e). Patent application Ser. No. 11/344,941 is also acontinuation-in-part application based on a prior conventionalapplication Ser. No. 11/123,610, filed on May 4, 2005, which issued asU.S. Pat. No. 7,450,229 on Nov. 11, 2008, which itself is based on aprior provisional application Ser. No. 60/567,911, filed on May 4, 2004,and which is also a continuation-in-part of prior patent applicationSer. No. 10/628,662, filed on Jul. 28, 2003, which issued as U.S. Pat.No. 6,975,400 on Dec. 13, 2005, which itself is a continuation-in-partapplication of prior patent application Ser. No. 09/976,257, filed onOct. 12, 2001, which issued as U.S. Pat. No. 6,608,682 on Aug. 19, 2003,which itself is a continuation-in-part application of prior patentapplication Ser. No. 09/820,434, filed on Mar. 29, 2001, which issued asU.S. Pat. No. 6,473,176 on Oct. 29, 2002, which itself is acontinuation-in-part application of prior patent application Ser. No.09/538,604, filed on Mar. 29, 2000, which issued as U.S. Pat. No.6,211,955 on Apr. 3, 2001, which itself is a continuation-in-partapplication of prior patent application Ser. No. 09/490,478, filed onJan. 24, 2000, which issued as U.S. Pat. No. 6,249,341 on Jun. 19, 2001,which itself is based on prior provisional patent application Ser. No.60/117,203, filed on Jan. 25, 1999, the benefit of the filing dates ofwhich is hereby claimed under 35 U.S.C. §120 and 35 U.S.C. §119(e).Patent application Ser. No. 09/976,257, noted above, is also based onprior provisional application Ser. No. 60/240,125, filed on Oct. 12,2000, the benefit of the filing date of which is hereby claimed under 35U.S.C. §119(e).

GOVERNMENT RIGHTS

This invention was funded at least in part with a grant (No. R43 CA94590-01) from the National Cancer Institute, and the U.S. governmentmay have certain rights in this invention.

BACKGROUND

Cellular hematopathologies have been traditionally identified andstudied by a variety of slide based techniques that includemorphological analysis of May-Grunwald/Giemsa or Wright/Giemsa stainedblood films and cytoenzymology. Additionally, other techniques, such ascell population analysis by flow cytometry, and molecular methods, suchas polymerase chain reaction (PCR) or in situ hybridization to determinegene expression, gene mutations, chromosomal translocations andduplications, have added to the understanding of these pathologies.

Although progress has been made using such techniques in advancingdiagnostic capabilities, understanding the mechanisms and theprogression of disease, as well as evaluating new therapeutics, suchtechnologies each offer challenges with regard to standardization androbustness, and to a large degree, they have not yet evolved to becomeroutine laboratory tests.

The conventional hematology clinical laboratory includes technologies torapidly and automatically analyze large numbers of samples of peripheralblood, with minimal human intervention. Companies such as AbbottLaboratories (Abbott Park, Ill.), Beckman Coulter Inc. (Fullerton,Calif.), and TOA Corporation (Kobe, Japan) continue to advance thesetechnologies with regard to throughput levels, the degree of accuracy ofthe analysis, as well as moderately increasing the information contentgathered in each sample run. However, in regard to any sample suggestiveof a cellular hematopathology, i.e., falling outside the accepted degreeof variance for any particular parameter, traditional slide basedmethodologies are largely used to determine the probable cause of theabnormality.

Diagnostic criteria in hematology are based on the morphologicalidentification of abnormalities in cell numbers, size, shape andstaining patterns. Although these have been supplemented over the pastdecades with cell population analysis, by staining with monoclonalantibodies to various cell surface determinants and acquiring data viaflow cytometry, the most important element in the diagnostic evaluationis the visual inspection of the peripheral blood film, bone marrow andlymph node biopsy using a microscope, which enables a subjectivecategorization of putative abnormalities.

The manual evaluation of tissue and blood films from patients istedious, time consuming, and subject to significant intra-laboratory andintra-observer variability. This process suffers from many sources ofvariability and error, including staining variability (which adverselyaffects longitudinal analysis), bias of the evaluator, and suboptimalsample preparation (blood films with increased “smudge” cells andatypical lymphocytes). The manual classification of a few hundred cellsby morphological appearance results in poor statistical power and lowconfidence in evaluating observed changes over time, or as a result oftreatment.

Chronic lymphocytic leukemia (CLL) is a type of cancer in which the bonemarrow produces an excess of lymphocytes (a type of white blood cell)due to a malignant transformation event (e.g., chromosomaltranslocation). CLL is the most frequent type of leukemia in the Westernworld. Normally, stem cells (immature cells) develop into mature bloodcells by a process of ordered differentiation, which occurs in the bonemarrow. There are three types of mature blood cells: (1) red blood cellsthat carry oxygen to all tissues of the body; (2) white blood cells thatfight infection; and, (3) platelets that help prevent bleeding byforming blood clots. Normally, the numbers and types of these bloodcells are tightly regulated. In CLL, there is a chronic pathologicaloverproduction of a type of white blood cell called lymphocytes. Thereare three types of lymphocytes: (1) B lymphocytes that make antibodiesto help fight infection; (2) T lymphocytes that help B lymphocytes makeantibodies to fight infection; and, (3) killer cells that attack cancercells and viruses. CLL is a disease involving an increase in Blymphocyte cell numbers in the peripheral blood, usually reflective of aclonal expansion of a malignantly transformed CD5+ B lymphocyte cell.

Currently, established chemotherapeutic treatments are used to treatthis condition, but a number of newer therapeutics, involving monoclonalantibodies to cell surface antigens expressed on CLL cells (e.g.,Rituximab), have been developed. Recent data from the National CancerData Base indicate that the 5-year survival for this disease conditionis about 48%, with only 23% of patients surviving the disease conditionafter 10 years. Recently, a number of prognostic factors have beenidentified that allow stratification of the patient population into twosubpopulations with distinct clinical outcomes. Factors that tend tocorrelate with decreased survival are: ZAP70 expression (a tyrosinekinase required for T lymphocyte cell signaling), increased CD38expression, un-mutated Ig Vh genes, and chromosomal abnormalities.However, routine assessment of these factors has not evolved to astandard clinical practice, due to technical challenges with datastandardization and interpretation.

Morphological evaluation remains the “gold standard” in the assessmentof hematopathologies, and patients with CLL present with morphologicalheterogeneity. Attempts to correlate a particular morphological profilewith clinical prognosis have been made, but to date, no association hasbeen widely accepted, and the morphologic sub-classification of CLL andits correlation with clinical prognosis remains to be explored.

It would therefore be desirable to provide a method and apparatussuitable for automatically analyzing blood, including peripheral bloodleukocytes, and cellular components such as bone marrow and lymph nodes(whose cells are readily amenable to being processed in suspension), tofacilitate researching blood related diseases and abnormalities. Itwould be particularly desirable to provide a method and apparatus forrapidly collecting imagery from blood and other bodily fluids (andcellular compartments), and to provide software tools for analyzing suchimagery to identify cellular abnormalities or cellular distributionabnormalities associated with a disease condition.

SUMMARY

This application specifically incorporates by reference the disclosuresand drawings of each patent application and each issued patentidentified above as a related application.

Aspects of the concepts disclosed herein relate to the collection ofmultispectral images from a population of cells, and the analysis of thecollected images to measure at least one characteristic of thepopulation, using photometric and/or morphometric image featurescalculated from the collection of images, where the image feature isassociated with a disease condition. In an exemplary application, thecells are obtained from bodily fluids and cellular compartments, and ina particularly preferred implementation, from blood, most preferablywhere the cellular compartments are bone marrow and lymph nodes. In afurther particularly preferred implementation, both photometric andmorphometric image features are used in the analysis. In a particularlypreferred, but not limiting implementation, the plurality of images foreach individual object are collected simultaneously.

Exemplary steps that can be used to analyze biological cells in accordwith an aspect of the concepts disclosed herein includes collectingimage data from a population of cells, and identifying one or moresubpopulations of cells from the image data. In one implementation, asubpopulation corresponding to cells exhibiting abnormalities associatedwith a disease condition is identified. Such subpopulations can beidentified based on empirical evidence indicating that one or morephotometric and/or morphometric image features are typically associatedwith the cellular abnormality associated with the disease condition. Theterm “image feature” is intended to refer to a calculated value thatquantitatively characterizes a particular structure, region, visualproperty, biochemical abundance, biochemical location, or other aspectof a cell that can be readily discerned from one or more images of thecell. The photometric and/or morphometric image features calculated fromthe collected images are analyzed to enable at least one characteristicof a cell or population of cells to be measured. Cellularcharacteristics that have been empirically associated with the cellularabnormalities present during a particular disease condition (e.g. anincrease in expression of a particular cell surface protein (which canbe labeled with a marker) as measured using a photometric “intensity”image feature or an increase in cell size as measured using amorphometric “cell area” image feature) can be detected in the data todetermine whether a particular disease condition is present in thepopulation of cells originally imaged.

In yet another exemplary implementation, a disease condition may bedetected even when the cells themselves do not exhibit any abnormalitiesthat can be identified by photometric and/or morphometric imagefeatures. In such an implementation, a sample will include a pluralityof different subpopulations, each of which is identified by its normalcharacteristic morphometric and photometric image features. Where adisease condition is not present, the ratio of the subpopulationsrelative to one another will vary within a determinable range acrossdifferent patients. Where a disease condition is present, the diseasecondition can alter the ratio of subpopulations, such that a change inthe ratio beyond a normal range can indicate the presence of a diseasecondition.

Consider a population of blood cells from a healthy patient. The ratioof lymphocytes to other types of blood cells can be determined byanalyzing image data of the entire population of blood cells to classifythe images according to blood cell type. When this same process isapplied to a population of blood cells from a patient with CLL, theratio of lymphocytes to other types of blood cells will be significantlydifferent than the ratio identified in a patient not afflicted with CLL.Thus, a disease condition can be detected by analyzing a population ofcells to identify subpopulations present in the population, and bydetermining changes in the ratios of the subpopulations that suggest thepresence of a disease condition.

In yet another exemplary implementation, a disease condition may bedetected by the presence of an uncharacteristic cell type. In such animplementation, a sample will include a plurality of differentsubpopulations, each of which is identified by its characteristicmorphometric and photometric image features. Where a disease conditionis not present, only the expected subpopulations will be evident withinthe sample, though they may vary within a normal range across differentpatients. Where a disease condition is present, an entirely atypicalcell type may be evident in the sample. For example, metastatic cancerof the breast may be evidenced by the presence of distinctive epithelialcells at some level in the blood. Thus, a disease condition can bedetected by analyzing a population of cells to identify subpopulationspresent in the population, and determining the prevalence of atypicalsubpopulations that suggest the presence of a disease condition. Thedisease condition may be further refined by analyzing the morphometricand photometric image features of the atypical cell population todetermine its tissue of origin or metastatic state. For example, thepresence of a large fraction of rapidly dividing cells, as evidenced bya high nuclear to cellular size ratio image feature, may characterize acirculating tumor cell as aggressive.

In still another exemplary implementation, a disease condition may bedetected by the analysis not only of the cell subpopulations and theirrelative abundance, but also by an analysis of free (notcell-associated) bio-molecules within the cell sample. In such animplementation, a reagent may be added to the cell sample, the reagentcomprising reactive substrates, each of which indicates the amount of aparticular bio-molecule present in the sample. Each reactive substrate(e.g., a microsphere) includes a unique optical signature thatidentifies the species of bio-molecule to which it preferentially binds,as well as potentially indicating the amount of that bio-molecule in thesample. By analyzing the imagery of a co-mingled sample of reactivesubstrates and cells, the former may be distinguished from the latter,and both a molecular and cellular analysis can be performed on thesample in a multiplexed fashion.

Image data for the population and subpopulation(s) can be manipulatedusing several different techniques. An exemplary technique is referredto as gating, which is a method of graphically defining a sub-populationof cells on a histogram or scatter plot of photometric or morphometriccell image features for a given cell population. A further exemplarytechnique is backgating, in which a previously-defined sub-population isgraphically highlighted on a histogram or scatter plot of photometric ormorphometric cell image features of a cell population. While notstrictly required, signal processing is preferably performed on thecollected image data to reduce crosstalk and enhance spatial resolution,particularly for image data collected using simultaneous multi-channelimaging.

In an exemplary implementation, image data is collected from twodifferent populations of cells (noting that image data of either of thetwo different populations can also be compared to image data of othercell populations if desired). The image data is analyzed to identifyimage features that quantify differences between the two different cellpopulations. Many different strategies can be employed in selecting thetwo different cell populations. In some embodiments, the first cellpopulation will include some known anomaly, and the second populationwill be known to be normal (or at least known to correspond to abaseline cell population, where the anomalous cell population is somehowmanipulated or exposed to some factor, and the baseline cell populationhas not been similarly manipulated or exposed), enabling differencesbetween the two populations to be quantified using the image data. Theanomaly can include, but is not limited to, the presence of neoplasticcells, the presence of necrotic cells, the presence of cells exposed toa toxic agent, the presence of cells exposed to a therapeutic agent, thepresence of cells exposed to a stimulating agent, the presence of cellsexposed to a chemical agent, the presence of cells exposed to a viralagent, the presence of cells exposed to a bacterial agent, the presenceof cells exposed to a nutrient, the presence of cells exposed to anenvironmental change, and the presence of different cells whose relativeabundance is associated with an anomalous condition.

The two different populations of cells can be acquired from differentsources. For example, the anomalous cell population can be acquired froma person suffering some condition, and the baseline or normal cellpopulation can be acquired from a healthy person. In an other example,the anomalous cell population can be acquired from a prepubescent maleor female, and the baseline or normal cell population can be acquiredfrom a post pubescent male or female (noting that in this case, it doesnot matter whether the prepubescent cells or adult cells are consideredto represent the anomalous or baseline population).

The two different populations of cells can be acquired from the samesource, at the same time, with the anomalous cell population beingmanipulated or exposed to some agent, and the baseline or normal cellpopulation not being similarly manipulated or exposed.

The two different populations of cells can be acquired from the samesource, at different times, to aid in quantifying cellular changes overtime. In this case, it does not matter whether the relatively oldersample or the relatively newer sample is considered to represent theanomalous or baseline population. In addition to simply a passage oftime, some other factor may contribute to some change in the cellpopulations. The factor can include a change in diet, a change instress, a change in environmental conditions, a change in health,exposure to environmental factors, exposure to therapeutic agents,exposure to toxins, exposure to viruses, exposure to infectious agents,and many other factors.

Yet another aspect of the techniques disclosed herein relates tomonitoring the treatment of a patient exhibiting a disease condition.Baseline data are collected by imaging a population of cells from thepatient before treatment. For example, the population of cells can beobtained from a bodily fluid, such as blood. During the course oftreatment, additional data are obtained by imaging additionalpopulations of cells collected from the patient during and after variousstages of the treatment process. Such data will provide a quantitativeindication of the improved condition of the patient suffering from thedisease condition, as indicated by either the amount of cells expressingthe disease condition versus normal cells, or by a change in a ratio ofthe subpopulations present in the population. Significantly, suchquantification is not feasible with standard microscopy and/orconventional flow cytometry.

In another exemplary implementation of the techniques disclosed herein,the imagery collected from a population of biological cells includescollection of multimodal images. That is, the images collected willinclude at least two of the following types of images: one or moreimages corresponding to light emitted from the cell (e.g. a fluorescenceimage), one or more images corresponding to light transmitted by thecell (e.g. a bright field image), and one or more images correspondingto light scattered by the cell (e.g. a dark field image). Such multimodeimaging can encompass any of the following types of images orcombinations thereof: (1) one or more fluorescence images and at leastone bright field image; (2) one or more fluorescence images and at leastone dark field image; (3) one or more fluorescence images, a brightfield image, and a dark field image; and (4) a bright field image and adark field image. Simultaneous collection of a plurality of differentfluorescence images (separated by spectrum) can also be beneficial, aswell as simultaneous collection of a plurality of different bright fieldimages (for example, using transmitted light with two different spectralfilters). The multimode images can preferably be collectedsimultaneously.

As discussed above, image data for a plurality of images of individualcells that are acquired simultaneously can be used to detect a diseasecondition. Note that such an application is based on identifying and/orquantifying differences between a first cell population and a secondcell population, by analyzing the image data collected for each cellpopulation. Generally, as described above, the image data can beanalyzed to identify quantifiable photometric and morphometricdifferences between the first and second cell populations. The imagedata can also be used to identify a cell type present in one of thefirst and second cell populations, but not the other of the first andsecond cell populations. Similarly, the image data can also be used toidentify differences in the relative distribution of cell types in thefirst and second cell populations, to determine if there is more or lessof a particular cell type in the first population of cells, as comparedto the second population of cells (and vice versa). These techniques canprovide diagnostic information about a patient from whom the cells areobtained, beyond simply determining if a specific disease conditionexists.

In one exemplary embodiment, the first and second population of cellsare obtained from a person at different times. In another exemplaryembodiment, the first and second population of cells are obtained from aperson at the same time, but then treated differently before beingimaged as described above. For example, a single blood sample or bodilyfluid sample can be acquired from a person, and that sample can be splitinto two fractions for different treatment prior to imaging. Image datafor the first fraction (the first population of cells) can be acquired.The second fraction (i.e., the second population of cells) can beexposed to a stimulus before image data are acquired. The image datafrom the first and second populations of cells can then be analyzed todetermine how the cell populations have changed.

In one related embodiment, image data from a first population of cellsand a second population of cells are analyzed to determine if variationsin a specific cell type present in both populations exist, regardless ofwhether those differences are indicative of a disease condition. Thistechnique is generally directed at acquiring the first and second cellpopulations from a person at different times, and determining if thereare differences between the same cell type in the first and secondpopulations due to changes over time. If data are available indicatingthe conditions experienced by the person during the time betweenacquiring the samples (e.g. a change in medication), then an attempt tocorrelate the changes to such conditions can be performed. Even where nosuch correlations can be found, any changes identified may be indicativeof changes in the health of the person. For example, some cellularchanges may suggest that the health of the patient has improved ordeclined, even if no specific disease condition is identified.Furthermore, even if no change in the first and second cell populationsis identified, that determination may itself represent valuablediagnostic data, either indicating that the health of the person has notappreciably changed, or if the person's health has changed, indicatingthat the specific cell type is likely not related to the change inhealth.

In another exemplary embodiment, image data from a first population ofcells and a second population of cells are analyzed to determine ifthere has been a change in the relative distribution of different typesof cells present in both populations, where such a change is not limitedto being indicative of a specific disease condition, but may still berelevant to the health of the person from whom the first and second cellpopulations were obtained. This analysis includes determining if aspecific cell type is present in the first cell population, but not thesecond cell population, and vice versa, as well as determining how therelative percentages of cell types present in both the first and secondcell populations has changed. This technique is generally directed atacquiring the first and second cell populations from a person atdifferent times, and determining if there are differences between thedistribution of different cell types in the first and secondpopulations. If data are available indicating the conditions experiencedby the person during the time between acquiring the samples, then anattempt to correlate the changes to such conditions can be performed.Even where no such correlations can be found, any changes identified maybe indicative of the health of the person. For example, some cellsignaling molecule distribution changes may suggest that the health ofthe patient has improved or declined, even if no specific diseasecondition is identified. Furthermore, even if no change in the cellsignaling molecule distributions in the first and second cellpopulations is identified, that itself may be valuable diagnostic data,either indicating that the health of the person has not appreciablychanged, or if the person's health has changed, indicating that themolecule distribution analyzed is likely not related to the change inhealth.

In another exemplary embodiment, image data from a first population ofcells and a second population of cells are analyzed to determine how thesecond population of cells responds to a stimulus not applied to thefirst population of cells, in order to either detect a disease conditionor to collect information relevant to the health of the person from whomthe populations of cells were obtained, without specifically identifyinga disease condition. In general, this technique is based on acquiringone sample from a person, and splitting that sample into two differentfractions (the two different cell populations can be acquired from theperson at different times, however doing so will introduce an additionalvariable). The first population of cells acts as a control. A stimulusis applied to the second population of cells so that the effect of thestimulus on the cells can be determined by comparing data collected forthe two populations.

This Summary has been provided to introduce a few concepts in asimplified form that are further described in detail below in theDescription. However, this Summary is not intended to identify key oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DRAWINGS

Various aspects and attendant advantages of one or more exemplaryembodiments and modifications thereto will become more readilyappreciated as the same becomes better understood by reference to thefollowing detailed description, when taken in conjunction with theaccompanying drawings, wherein:

FIG. 1A is a schematic diagram of an exemplary flow imaging system thatcan be used to simultaneously collect a plurality of images from anobject in flow;

FIG. 1B is a plan view of an exemplary flow imaging system that employsa spectral dispersion component comprising a plurality of stackeddichroic filters employed to spectrally separate the light tosimultaneously collect a plurality of images from an object in flow;

FIG. 1C illustrates an exemplary set of images projected onto the TDIdetector when using the spectral dispersing filter system of the FIG.1B;

FIG. 2 is a pictorial representation of an image recorded by the flowimaging system of FIG. 1;

FIG. 3 is a flow chart of the overall method steps implemented in oneaspect of the concepts disclosed herein;

FIG. 4 is an exemplary graphical user interface used to implement themethod steps of FIG. 3;

FIG. 5 is an exemplary graphical user interface used to implement themethod steps of FIG. 3 as applied to the analysis of human peripheralblood;

FIG. 6 includes images of normal (i.e., healthy) mammary epithelialcells;

FIG. 7 includes images of mammary carcinoma (i.e., diseased) cells,illustrating how quantification of data in a fluorescent channel servesas an image feature for the disease condition;

FIG. 8A is an exemplary graphical user interface used to implement themethod steps of FIG. 3, illustrating a plurality of differentphotometric and morphometric descriptors as shown in FIGS. 8B-8M thatcan be used to automatically distinguish images of healthy mammaryepithelial cells from images of mammary carcinoma cells;

FIG. 9 graphically illustrates the separation of cells in humanperipheral blood into a variety of subpopulations based on photometricproperties;

FIG. 10A graphically illustrates a distribution of normal peripheralblood mononuclear cells (PBMC) based on image data collected from apopulation of cells that do not include mammary carcinoma cells;

FIG. 10B graphically illustrates a distribution of normal PBMC andmammary carcinoma cells based on image data collected from a populationof cells that includes both cell types, illustrating how thedistribution of mammary carcinoma cells is distinguishable from thedistribution of the normal PBMC cells;

FIG. 11A graphically illustrates a distribution of normal PBMC andmammary carcinoma cells based on a measured cytoplasmic area derivedfrom image data collected from a population of cells that includes bothcell types, illustrating how the distribution of cytoplasmic area of themammary carcinoma cells is distinguishable from the distribution ofcytoplasmic area of the normal PBMC cells;

FIG. 11B graphically illustrates a distribution of normal PBMC andmammary carcinoma cells based on measured scatter frequency derived fromimage data collected from a population of cells that includes both celltypes, illustrating how the distribution of the scatter frequency of themammary carcinoma cells is distinguishable from the distribution of thescatter frequency of the normal PBMC cells;

FIG. 12 shows composite images of cells generated by combining brightfield and fluorescent images of mammary carcinoma cells;

FIG. 13 shows representative images of five different PBMC populationsthat can be defined by scatter data derived from image data of apopulation of cells;

FIG. 14 schematically illustrates an exemplary computing system used toimplement the method steps of FIG. 3; and

FIG. 15 is a flow chart illustrating exemplary steps for analyzing twopopulations of cells, based on images of the cell populations, in orderto identify and/or quantify differences between the cell populations.

DESCRIPTION Figures and Disclosed Embodiments are not Limiting

Exemplary embodiments are illustrated in referenced Figures of thedrawings. It is intended that the embodiments and Figures disclosedherein are to be considered illustrative rather than restrictive.

Overview

The present disclosure encompasses a method of using flow imagingsystems that can combine the speed, sample handling, and cell sortingcapabilities of flow cytometry with the imagery, sensitivity, andresolution of multiple forms of microscopy and full visible/nearinfrared spectral analysis to collect and analyze data relating todisease conditions in blood, particularly detecting and monitoringchronic lymphocytic leukemia.

An aspect of the concepts disclosed herein relates to a system andmethod for imaging and analyzing biological cells entrained in a flow offluid. In at least one embodiment, a plurality of images of biologicalcells are collected simultaneously; the plurality of images including atleast two of the following types of images: a bright field image, a darkfield image, and a fluorescent image. Images are collected for apopulation of biological cells. Once the imagery has been collected, theimages can be processed to identify a subpopulation of images, where thesubpopulation shares photometric and/or morphometric characteristicsempirically determined to be associated with a disease condition.

With respect to the following disclosure, and the claims that follow, itshould be understood that the term “population of cells” refers to agroup of cells including a plurality of objects. Thus, a population ofcells must include more than one cell.

A preferred imaging system to be used in collecting the image datarequired to implement the techniques disclosed herein will incorporatethe following principal characteristics:

1. high speed measurement;

2. the ability to process very large or continuous samples;

3. high spectral resolution and bandwidth;

4. good spatial resolution;

5. high sensitivity; and

6. low measurement variation.

In particular, a recently developed imaging flow cytometer technology,which is embodied in an ImageStream™ instrument (Amnis Corporation,Seattle, Wash.), makes great strides in achieving each of theabove-noted principle characteristics. The ImageStream™ instrument is acommercial embodiment of the flow imaging systems described below indetail with respect to FIG. 1. These significant advancements in the artof flow cytometery are described in the following commonly assignedpatents: U.S. Pat. No. 6,249,341, issued on Jun. 19, 2001 and entitled“Imaging And Analyzing Parameters of Small Moving Objects Such AsCells;” U.S. Pat. No. 6,211,955 issued on Apr. 3, 2001, also entitled“Imaging And Analyzing Parameters of Small Moving Objects Such AsCells;” U.S. Pat. No. 6,473,176, issued on Oct. 29, 2002, also entitled“Imaging And Analyzing Parameters of Small Moving Objects Such AsCells;” U.S. Pat. No. 6,583,865, issued on Jun. 24, 2003, entitled“Alternative Detector Configuration And Mode of Operation of A TimeDelay Integration Particle Analyzer;” U.S. patent application Ser. No.09/989,031 entitled “Imaging And Analyzing Parameters of Small MovingObjects Such As Cells in Broad Flat Flow.” While the ImageStream™platform represents a particularly preferred imaging instrument used toacquire the image data that will be processed in accord with theconcepts disclosed herein, it should be understood that the conceptsdisclosed herein are not limited only to the use of that specificinstrument.

As noted above, in addition to collecting image data from a populationof biological cells, an aspect of the concepts disclosed herein involvesprocessing the image data collected to measure at least onecharacteristic associated with a disease condition in the imagedpopulation. A preferred image analysis software package is IDEAS™ (AmnisCorporation, Seattle, Wash.). The IDEAS™ package evaluates nearly 200image features for every cell, including multiple morphologic andfluorescence intensity measurements, which can be used to define andcharacterize cell populations. The IDEAS™ package enables the user todefine biologically relevant cell subpopulations, and analyzesubpopulations using standard cytometry analyses, such as gating andbackgating. It should be understood, however, that other image analysismethods or software packages can be implemented to apply the conceptsdisclosed herein, and the preferred image analysis software package thatis disclosed is intended to be exemplary, rather than limiting of theconcepts disclosed herein.

Overview of a Preferred Imaging System

FIG. 1 is a schematic diagram of a preferred flow imaging system 510(functionally descriptive of the ImageStream™ platform) that uses TDIwhen capturing images of objects 502 (such as biological cells),entrained in a fluid flow 504. System 510 includes a velocity detectingsubsystem that is used to synchronize a TDI imaging detector 508 withthe flow of fluid through the system. Significantly, imaging system 510is capable of simultaneously collecting a plurality of images of anobject. A particularly preferred implementation of imaging system 510 isconfigured for multi-spectral imaging and can operate with six spectralchannels: DAPI fluorescence (400-460 nm), Dark field (460-500 nm), FITCfluorescence (500-560 nm), PE fluorescence (560-595 nm), Bright field(595-650 nm), and Deep Red (650-700 nm). The TDI detector can provide 10bit digital resolution per pixel. The numeric aperture of the preferredimaging system is typically 0.75, with a pixel size of approximately 0.5microns. However, those skilled in the art will recognize that this flowimaging system is neither limited to six spectral channels, nor limitedto either the stated aperture size or pixel size and resolution.

Moving objects 502 are illuminated using a light source 506. The lightsource may be a laser, a light emitting diode, a filament lamp, a gasdischarge arc lamp, or other suitable light emitting source, and thesystem may include optical conditioning elements such as lenses,apertures, and filters that are employed to deliver broadband or one ormore desired wavelengths or wavebands of light to the object with anintensity required for detection of the velocity and one or more othercharacteristics of the object. Light from the object is split into twolight paths by a beam splitter 503. Light traveling along one of thelight paths is directed to the velocity detector subsystem, and lighttraveling along the other light path is directed to TDI imaging detector508. A plurality of lenses 507 are used to direct light along the pathsin a desired direction, and to focus the light. Although not shown, afilter or a set of filters can be included to deliver to the velocitydetection subsystem and/or TDI imaging detector 508, only a narrow bandof wavelengths of the light corresponding to, for example, thewavelengths emitted by fluorescent or phosphorescent molecules in/on theobject, or light having the wavelength(s) provided by the light source506, so that light from undesired sources is substantially eliminated.

The velocity detector subsystem includes an optical grating 505 a thatamplitude modulates light from the object, a light sensitive detector505 b (such as a photomultiplier tube or a solid-state photodetector), asignal conditioning unit 505 c, a velocity computation unit 505 d, and atiming control unit 505 e, which assures that TDI imaging detector 508is synchronized to the flow of fluid 504 through the system. The opticalgrating preferably comprises a plurality of alternating transparent andopaque bars that modulate the light received from the object, producingmodulated light having a frequency of modulation that corresponds to thevelocity of the object from which the light was received. Preferably,the optical magnification and the ruling pitch of the optical gratingare chosen such that the widths of the bars are approximately the sizeof the objects being illuminated. Thus, the light collected from cellsor other objects is alternately blocked and transmitted through theruling of the optical grating as the object traverses the interrogationregion, i.e., the field of view. The modulated light is directed towarda light sensitive detector, producing a signal that can be analyzed by aprocessor to determine the velocity of the object. The velocitymeasurement subsystem is used to provide timing signals to TDI imagingdetector 508.

Preferably, signal conditioning unit 505 c comprises a programmablecomputing device, although an ASIC chip or a digital oscilloscope canalso be used for this purpose. The frequency of the photodetector signalis measured, and the velocity of the object is computed as a function ofthat frequency. The velocity dependent signal is periodically deliveredto a TDI detector timing control 505 e to adjust the clock rate of TDIimaging detector 508. Those of ordinary skill in the art will recognizethat the TDI detector clock rate is adjusted to match the velocity ofthe image of the object over the TDI detector to within a smalltolerance selected to ensure that longitudinal image smearing in theoutput signal of the TDI detector is within acceptable limits. Thevelocity update rate must occur frequently enough to keep the clockfrequency within the tolerance band as flow (object) velocity varies.

Beam splitter 503 has been employed to divert a portion of light from anobject 502 to light sensitive detector 505 b, and a portion of lightfrom object 502 a to TDI imaging detector 508. In the light pathdirected toward TDI imaging detector 508, there is a plurality ofstacked dichroic filters 509, which separate light from object 502 ainto a plurality of wavelengths. One of lenses 507 is used to form animage of object 502 a on TDI imaging detector 508.

The theory of operation of a TDI detector like that employed in system510 is as follows. As objects travel through a flow tube 511 (FIG. 1)and pass through the volume imaged by the TDI detector, light from theobjects forms images of the objects, and these images travel across theface of the TDI detector. The TDI detector preferably comprises a chargecoupled device (CCD) array, which is specially designed to allow chargeto be transferred on each clock cycle, in a row-by-row format, so that agiven line of charge remains locked to, or synchronized with, a line inthe image. The row of charge is clocked out of the array and into amemory when it reaches the bottom of the array. The intensity of eachline of the signal produced by the TDI detector corresponding to animage of an object is integrated over time as the image andcorresponding resulting signal propagate over the CCD array. Thistechnique greatly improves the signal-to-noise ratio of the TDI detectorcompared to non-integrating type detectors—a feature of great benefit ina detector intended to respond to images from low-level fluorescenceemission of an object. Proper operation of the TDI detector requiresthat the charge signal be clocked across the CCD array insynchronization with the rate at which the image of the object movesacross the CCD array. An accurate clock signal to facilitate thissynchronization can be provided by determining the velocity of theobject, and the concepts disclosed herein use an accurate estimate ofthe object's velocity, and thus, of the velocity of the image as itmoves over the CCD array of the TDI detector. A flow imaging system ofthis type is disclosed in commonly assigned U.S. Pat. No. 6,249,341, thecomplete disclosure, specification, and drawings of which are herebyspecifically incorporated herein by reference.

FIG. 2 is a pictorial representation of images produced by the flowimaging system of FIG. 1. A column 520, labeled “BF,” includes imagescreated by the absorption of light from light source 506 by sphericalobjects 502 entrained in fluid flow 504. The “BF” label refers to“bright field,” a term derived from a method for creating contrast in animage whereby light is passed through a region and the absorption oflight by objects in the region produces dark areas in the image. Thebackground field is thus bright, while the objects are dark in thisimage. Thus, column 520 is the “bright field channel.” It should beunderstood that the inclusion of a bright field image is exemplary,rather than limiting on the scope of the concepts disclosed herein.Preferably, the concepts disclosed herein utilize a combination ofbright field images and fluorescent images, or of dark field images andfluorescent images.

The remaining three columns 522, 524, and 526 shown in FIG. 2 arerespectively labeled “λ1,” “λ2,” and “λ3.” These columns include imagesproduced using light that has been emitted by an object entrained in thefluid flow. Preferably, such light is emitted through the process offluorescence (as opposed to images produced using transmitted light). Asthose of ordinary skill in the art will recognize, fluorescence is theemission of light (or other electromagnetic radiation) by a substancethat has been stimulated by the absorption of incident radiation.Generally, fluorescence persists only for as long as the stimulatingradiation persists. Many substances (particularly fluorescent dyes) canbe identified based on the spectrum of the light that is produced whenthey fluoresce. Columns 522, 524, and 526 are thus referred to as“fluorescence channels.”

Additional exemplary flow imaging systems are disclosed in commonlyassigned U.S. Pat. No. 6,211,955 and U.S. Pat. No. 6,608,682, thecomplete disclosure, specification, and drawings of which are herebyspecifically incorporated herein by reference as background material.The imaging systems described above and in these two patents in detail,and incorporated herein by reference, have substantial advantages overmore conventional systems employed for the acquisition of images ofbiological cell populations. These advantages arise from the use inseveral of the imaging systems of an optical dispersion system, incombination with a TDI detector that produces an output signal inresponse to the images of cells and other objects that are directed ontothe TDI detector. Significantly, multiple images of a single object canbe collected at one time. The image of each object can be spectrallydecomposed to discriminate object characteristics by absorption,scatter, reflection, or emissions, using a common TDI detector for theanalysis. Other systems include a plurality of detectors, each dedicatedto a single spectral channel.

These imaging systems can be employed to determine morphological,photometric, and spectral characteristics of cells and other objects bymeasuring optical signals including light scatter, reflection,absorption, fluorescence, phosphorescence, luminescence, etc.Morphological parameters include area, perimeter, texture or spatialfrequency content, centroid position, shape (i.e., round, elliptical,barbell-shaped, etc.), volume, and ratios of selected pairs (or subsets)of these parameters. Similar parameters can also be determined for thenuclei, cytoplasm, or other sub-compartments of cells with the conceptsdisclosed herein. Photometric measurements with the preferred imagingsystem enable the determination of nuclear optical density, cytoplasmoptical density, background optical density, and ratios of selectedpairs of these values. An object being imaged with the conceptsdisclosed herein can either be stimulated into fluorescence orphosphorescence to emit light, or may be luminescent, producing lightwithout stimulation. In each case, the light from the object is imagedon the TDI detector to use the concepts disclosed herein to determinethe presence and amplitude of the emitted light, the number of discretepositions in a cell or other object from which the light signal(s)originate(s), the relative placement of the signal sources, and thecolor (wavelength or waveband) of the light emitted at each position inthe object.

Using a Multispectral Imaging System to Analyze Bodily Fluid for aDisease Condition

As noted above, aspects of the concepts disclosed herein involve boththe collection of multispectral images from a population of biologicalcells, and the analysis of the collected images to identify at least onephotometric or morphological image feature that has been empiricallydetermined to be associated with a disease condition. Thus, an aspect ofthe present disclosure relates to the use of both photometric andmorphometric image features derived from multi-mode imagery of objects(e.g., cells) in flow to discriminate cell characteristics inpopulations of cells, to facilitate the detection of the presence of adisease condition. Discussed in more detail below are methods foranalyzing cells in suspension or flow, which may be combined withcomprehensive multispectral imaging to provide morphometric andphotometric data to enable, for example, the quantization ofcharacteristics exhibited by both normal cells and diseased cells, tofacilitate the detection of diseased or abnormal cells indicative of adisease condition. Heretofore, such methods have not been feasible withstandard microscopy and/or flow cytometry.

As noted above, a preferred flow imaging system (e.g., the ImageStream™platform) can be used to simultaneously acquire multispectral images ofcells in flow, to collect image data corresponding to bright field, darkfield, and four channels of fluorescence. The ImageStream™ platform is acommercial embodiment based on the imaging systems described in detailabove. In general, cells are hydrodynamically focused into a core streamand orthogonally illuminated for both dark field and fluorescenceimaging. The cells are simultaneously trans-illuminated via aspectrally-limited source (e.g., filtered white light or a lightemitting diode) for bright field imaging. Light is collected from thecells with an imaging objective lens and is projected on a CCD array.The optical system has a numeric aperture of 0.75 and the CCD pixel sizein object space is 0.5μ², enabling high resolution imaging at eventrates of approximately 100 cells per second. Each pixel is digitizedwith 10 bits of intensity resolution in this example, providing aminimum dynamic range of three decades per pixel. In practice, thespread of signals over multiple pixels results in an effective dynamicrange that typically exceeds four decades per image. Additionally, thesensitivity of the CCD can be independently controlled for eachmultispectral image, resulting in a total of approximately six decadesof dynamic range across all the images associated with an object. Itshould be understood that while the ImageStream™ platform represents aparticularly preferred flow imaging system for acquiring image data inaccord with the concepts disclosed herein, the ImageStream™ platform isintended to represent an exemplary imaging system, rather than limitingthe concepts disclosed. Any imaging instrument capable of collectingimages of a population of biological cells sufficient to enable theimage analysis described in greater detail below to be achieved can beimplemented in accord with the concepts presented herein.

Referring again to the preferred imaging system, the ImageStream™platform, prior to projection on the CCD, the light is passed through aspectral decomposition optical system that directs different spectralbands to different lateral positions across the detector (such spectraldecomposition is discussed in detail above in connection with thedescription of the various preferred embodiments of imaging systems).With this technique, an image is optically decomposed into a set of aplurality of sub-images (preferably 6 sub-images, including: brightfield, dark field, and four different fluorescent images), eachsub-image corresponding to a different spectral (i.e., color) componentand spatially isolated from the remaining sub-images. This processfacilitates identification and quantization of signals within the cellby physically separating on the detector signals that may originate fromoverlapping regions of the cell. Spectral decomposition also enablesmultimode imaging, i.e., the simultaneous detection of bright field,dark field, and multiple colors of fluorescence. The process of spectraldecomposition occurs during the image formation process, rather than viadigital image processing of a conventional composite image.

The CCD may be operated using TDI to preserve sensitivity and imagequality even with fast relative movement between the detector and theobjects being imaged. As with any CCD, image photons are converted tophoto charges in an array of pixels. However, in TDI operation, thephoto charges are continuously shifted from pixel to pixel down thedetector, parallel to the axis of flow. If the photo charge shift rateis synchronized with the velocity of the image of the cell, the effectis similar to physically panning a camera. Image streaking is avoideddespite signal integration times that are orders of magnitude longerthan in conventional flow cytometry. For example, an instrument mayoperate at a continuous data rate of approximately 30 mega pixels persecond and integrate signals from each object for 10 milliseconds,enabling the detection of even faint fluorescent probes within cellimages to be acquired at relatively high speed. Careful attention topump and fluidic system design to achieve highly laminar, non-pulsatileflow eliminates any cell rotation or lateral translation on the timescale of the imaging process (see, e.g., U.S. Pat. No. 6,532,061).

A real-time algorithm analyzes every pixel read from the CCD to detectthe presence of object images and calculate a number of basicmorphometric and photometric image features, which can be used ascriteria for data storage. Data files encompassing 10,000-20,000 cellsare typically about 100 MB in size and, therefore, can be stored andanalyzed using standard personal computers. The TDI readout processoperates continuously without any “dead time,” which means every cellcan be imaged and the coincidental imaging of two or more cells at atime either in contact or not, presents no barrier to data acquisition.

Such an imaging system can be employed to determine morphological,photometric, and spectral characteristics of cells and other objects bymeasuring optical signals, including light scatter, reflection,absorption, fluorescence, phosphorescence, luminescence, etc. As usedherein, morphological parameters (i.e., morphometrics) may be basic(e.g., nuclear shape) or may be complex (e.g., identifying cytoplasmsize as the difference between cell size and nuclear size). For example,morphological parameters may include nuclear area, perimeter, texture orspatial frequency content, centroid position, shape (i.e., round,elliptical, barbell-shaped, etc.), volume, and ratios of selected pairsof these parameters. Morphological parameters of cells may also includecytoplasm size, texture or spatial frequency content, volume, and thelike. As used herein, photometric measurements with the aforementionedimaging system can enable the determination of nuclear optical density,cytoplasm optical density, background optical density, and the ratios ofselected pairs of these values. An object being imaged can be stimulatedinto fluorescence or phosphorescence to emit light, or may beluminescent, wherein light is produced by the object withoutstimulation. In each case, the light from the object may be imaged on aTDI detector of the imaging system to determine the presence andamplitude of the emitted light, the number of discrete positions in acell or other object from which the light signal(s) originate(s), therelative placement of the signal sources, and the color (wavelength orwaveband) of the light emitted at each position in the object.

The present disclosure provides methods of using both photometric andmorphometric image features derived from multi-mode imagery of objectsin flow. Such methods can be employed as a cell analyzer to determine ifone or more image features corresponding to a disease condition ispresent in the population of cells imaged. As noted above, certain imagefeatures can be indicative of the cellular abnormality associated with adisease condition, or image features can be indicative of a change in aratio of subpopulations present in the population of the cells imaged,where the change in ratio is indicative of a disease condition.Preferably the population of cells is imaged while entrained in a fluidflowing through an imaging system. As used herein, gating refers to asubset of data relating to photometric or morphometric imaging. Forexample, a gate may be a numerical or graphical boundary of a subset ofdata that can be used to define the characteristics of particles to befurther analyzed. Here, gates have been defined, for example, as a plotboundary that encompasses “in focus” cells, or sperm cells with tails,or sperm cells without tails, or cells other than sperm cells, or spermcell aggregates, or cell debris. Further, backgating may be a subset ofthe subset data. For example, a forward scatter versus a side scatterplot in combination with a histogram from an additional image featuremay be used to backgate a subset of cells within the initial subset ofcells.

In using an imaging system as described herein, it should be made clearthat a separate light source is not required to produce an image of theobject (cell), if the object is luminescent (i.e., if the objectproduces light). However, many of the applications of an imaging systemas described herein will require that one or more light sources be usedto provide light that is incident on the object being imaged. A personhaving ordinary skill in the art will know that the locations of thelight sources substantially affect the interaction of the incident lightwith the object and the kind of information that can be obtained fromthe images using a detector.

In addition to imaging an object with the light that is incident on it,a light source can also be used to stimulate emission of light from theobject. For example, a cell having been contacted with a probeconjugated to a fluorochrome (e.g., such as FITC, PE, APC, Cy3, Cy5, orCy5.5) will fluoresce when excited by light, producing a correspondingcharacteristic emission spectra from any excited fluorochrome probe thatcan be imaged on a TDI detector. Light sources may alternatively be usedfor causing the excitation of fluorochrome probes on an object, enablinga TDI detector to image fluorescent spots produced by the probes on theTDI detector at different locations as a result of the spectraldispersion of the light from the object that is provided by a prism. Thedisposition of these fluorescent spots on the TDI detector surface willdepend upon their emission spectra and their location in the object.

Each light source may produce light that can either be coherent,non-coherent, broadband, or narrowband light, depending upon theapplication of the imaging system desired. Thus, a tungsten filamentlight source can be used for applications in which a narrowband lightsource is not required. For applications such as stimulating theemission of fluorescence from probes, narrowband laser light ispreferred, since it also enables a spectrally decomposed, non-distortedimage of the object to be produced from light scattered by the object.This scattered light image will be separately resolved from thefluorescent spots produced on a TDI detector, so long as the emissionspectra of any of the spots are at different wavelengths than thewavelength of the laser light. The light source can be either of thecontinuous wave (CW) or pulsed type, such as a pulsed laser. If a pulsedtype illumination source is employed, the extended integration periodassociated with TDI detection can enable the integration of signals frommultiple pulses. Furthermore, it is not necessary for the light to bepulsed in synchronization with the TDI detector.

Particularly for use in collecting image data for cell populations foundin bodily fluids such as blood, it can be desirable to employ a 360 nmUV laser as a light source, and to optimize the optical system of theimaging system for diffraction-limited imaging performance in the400-460 nm (DAPI emission) spectral band.

In embodiments consistent with the disclosure herein, it is to beunderstood that relative movement exists between the object being imagedand the imaging system. In most cases, it will be more convenient tomove the object than to move the imaging system. It is also contemplatedthat in some cases, the object may remain stationary and the imagingsystem move relative to it. As a further alternative, both the imagingsystem and the object may be in motion, which movement may be indifferent directions and/or at different rates.

Exemplary Imaging System and Detector

While the principles of preferred imaging systems have been discussedabove, the following provides a more detailed description of anexemplary imaging system, and an exemplary detector, in order todescribe how the imaging optics and detector cooperate to achieve thesimultaneous collection of a plurality of images.

The following imaging system employs a spectral dispersion filterassembly that does not convolve the acquired images with the emissionspectra of the light forming the images, thereby eliminating the needfor deconvolution of the emission spectra from the image. FIG. 1Billustrates a non-distorting spectral dispersion system 250 that employsa five color stacked wedge spectral dispersing filter assembly 252.

In FIG. 1B (which is a plan view), a fluid flow 22 entrains an object 24(such as a cell, but alternatively, a small particle) and carries theobject through the imaging system. The direction of the fluid flow inFIG. 1B is into (or out of) the drawing sheet. Light 30 from object 24passes through collection lenses 32 a and 32 b that collect the light,producing collected light 253, which is approximately focused atinfinity, i.e. the rays of collected light from collection lens 32 b aregenerally parallel. Collected light 253 enters spectral dispersingfilter assembly 252, which disperses the light, producing dispersedlight 257. The dispersed light then enters imaging lenses 40 a and 40 b,which focuses light 257 onto a TDI detector 44.

The spectral dispersing filter assembly splits the light into aplurality of light beams having different bandwidths. Each light beamthus produced is directed at a different nominal angle so as to fallupon a different region of TDI detector 44. The nominal angularseparation between each bandwidth produced by the spectral dispersingfilter assembly 252 exceeds the field angle of the imaging system inobject space thereby preventing overlap of the field images of variousbandwidths on the detector.

Spectral dispersing filter assembly 252 comprises a plurality of stackeddichroic wedge filters, including a red dichroic filter R, an orangedichroic filter O, a yellow dichroic filter Y, a green dichroic filterG, and a blue dichroic filter B. Red dichroic filter R is placed in thepath of collected light 34, oriented at an angle of approximately 44.0°relative to an optic axis 253 of collection lenses 32 a and 32 b. Lightof red wavelengths and above, i.e., >640 nm, is reflected from thesurface of red dichroic filter R at a nominal angle of 1°, measuredcounter-clockwise from a vertical optic axis 257. The light reflected byred dichroic filter R leaves spectral dispersing filter assembly 252 andpasses through imaging lenses 40 a and 40 b, which cause the light to beimaged onto a red light receiving region of TDI detector 44, which isdisposed toward the right end of the TDI detector, as shown in FIG. 1B.

Orange dichroic filter O is disposed a short distance behind reddichroic filter R and is oriented at an angle of 44.5 degrees withrespect to optic axis 253. Light of orange wavelengths and greater,i.e., >610 nm, is reflected by orange dichroic filter O at a nominalangle of 0.5° with respect to vertical optic axis 257. Because theportion of collected light 34 comprising wavelengths longer than 640 nmwas already reflected by red dichroic filter R, the light reflected fromthe surface of orange dichroic filter O is effectively bandpassed in theorange colored region between 610 nm and 640 nm. This light travels at anominal angle of 0.5° from vertical optic axis 257, and is imaged byimaging lenses 40 a and 40 b so as to fall onto an orange lightreceiving region disposed toward the right hand side of TDI detector 44between a center region of the TDI detector and the red light receivingregion, again as shown in FIG. 1B.

Yellow dichroic filter Y is disposed a short distance behind orangedichroic filter O and is oriented at an angle of 45° with respect tooptic axis 253. Light of yellow wavelengths, i.e., 560 nm and longer, isreflected from yellow dichroic filter Y at a nominal angle of 0.0° withrespect to vertical optic axis 257. Wavelengths of light reflected byyellow dichroic filter Y are effectively bandpassed in the yellow regionbetween 560 nm and 610 nm and are imaged by imaging lenses 40 a and 40 bnear vertical optic axis 257 so as to fall on a yellow light receivingregion toward the center of TDI detector 44.

In a manner similar to dichroic filters R, O, and Y, dichroic filters Gand B are configured and oriented so as to image green and blue lightwavebands onto respective green and blue light receiving regions of TDIdetector 44, which are disposed toward the left-hand side of the TDIdetector. By stacking the dichroic filters at different predefinedangles, spectral dispersing filter assembly 252 collectively works tofocus light within predefined wavebands of the light spectrum ontopredefined regions of TDI detector 44.

The wedge shape of the dichroic filters in the preceding discussionallows the filters to be placed in near contact, in contact or possiblycemented together to form the spectral dispersing filter assembly 252.The angle of the wedge shape fabricated into the substrate for thedichroic filter allows easy assembly of the spectral dispersing filterassembly 252, forming a monolithic structure in which the wedge-shapedsubstrate is sandwiched between adjacent dichroic filters. If thefilters are in contact with each other or cemented together, thecomposition of the materials that determine the spectral performance ofthe filter may be different from those which are not in contact. Thoseof ordinary skill in the art will appreciate that flat, non wedge-shapedsubstrates could be used to fabricate the spectral dispersing filterassembly 252. In this case another means such as mechanically mountingthe filters could be used to maintain the angular relationships betweenthe filters.

In addition to the foregoing configuration, non-distorting spectraldispersion system 250 may optionally include a detector filter assembly254 to further attenuate undesired signals in each of the light beams,depending upon the amount of rejection required for out-of-band signals.In the embodiment shown in FIG. 1B, light may pass through each dichroicfilter in the spectral dispersing filter assembly 252 twice beforeexiting the spectral dispersing filter assembly 252. This condition willfurther attenuate out-of-band signals, but will also attenuate in-bandsignals.

The foregoing description illustrates the use of a five color system.Those skilled in the art will appreciate that a spectral dispersingcomponent with more or fewer filters may be used in these configurationsin order to construct a system covering a wider or a narrower spectralregion, or different passbands within a given spectral region. Likewise,those skilled in the art will appreciate that the spectral resolution ofthe present invention may be increased or decreased by appropriatelychoosing the number and spectral characteristics of the dichroic and orbandpass filters that are used. Furthermore, those skilled in the artwill appreciate that the angles or orientation of the filters may beadjusted to direct light of a given bandwidth onto any desired point onthe TDI detector. In addition, there is no need to focus the light inincreasing or decreasing order by wavelength. For example, influorescence imaging applications, one may wish to create more spatialseparation on the TDI detector between the excitation and emissionwavelengths by changing the angles at which the filters corresponding tothose wavelengths are oriented with respect to the optic axes of thesystem. Finally, it will be clear to those skilled in the art thatdispersion of the collected light may be performed on the basis ofnon-spectral characteristics, including angle, position, polarization,phase, or other optical properties.

FIG. 1C illustrates the distribution of images on TDI detector 44corresponding to imaging a plurality of cells 280-284 usingnon-distorting spectral dispersion system 250. Significantly, the fieldangle of system 250 is orthogonal to flow in object space, such that theindividual images are laterally dispersed across detector 44 (asindicated on FIG. 1C), substantially orthogonal to a direction of amotion of the respective images across the TDI detector (i.e., theobject moves vertically across the detector, and the plurality of imagesare dispersed horizontally across the detector).

In this particular configuration, the field angle in object space isless than +/−0.25°. Those skilled in the art will appreciate that thefield angle can be made larger or smaller. To the extent that the fieldangle is made larger, for example, to image cells over a wider region ona slide or in a broad flat flow, the field angle at the detector willincrease in proportion to the number of colors used. FIG. 1C illustratesthe image projected onto the detector when three cells 280, 282 and 284are flowing through the field of view. Light scatter images of cells280, 282, and 284 are seen on the left hand side of the detector denotedas the BLUE area. Images of cell nuclei 202 stained with a greenfluorescent dye are seen in the GREEN area of the detector. Threedifferently-colored genetic probes 204, 205, and 206 are also employedfor the analysis of the sex chromosomes within the cells. Probe 204stains the X chromosome with an orange fluorescing dye, probe 205 stainsthe Y chromosome with yellow fluorescing dye, and probe 206 stains theinactive X chromosome in female cells with a red fluorescing dye. Cell282 is imaged onto the detector as shown in FIG. 1C. An image 286 ofprobe 204 from cell 282 is seen in the ORANGE area of the detector.Likewise an image 288 of probe 205 from cell 282 is seen in the YELLOWarea of the detector. The signal on the detector is processed todetermine the existence and position of these images on the detector todetermine that cell 282 is a male cell. In a similar manner, cells 280and 284 contain probes 204 and 206, which create images 290 and 292 inthe ORANGE area of the detector, and images 294 and 296 in the RED areaof the detector, indicating that these cells are female, respectively.

Exemplary High Level Method Steps

FIG. 3 is a flow chart 400 schematically illustrating exemplary stepsthat can be used to analyze a population of cells based on images of thecell population, in order to identify a disease condition. In aparticularly preferred embodiment, the cell population is obtained froma bodily fluid, such as blood. In a block 402, an imaging system, suchas the exemplary imaging system described above in detail, is used tocollect image data from a first population of biological cells where adisease condition is known to be present. In a block 404 at least onephotometric or morphometric image feature associated with the diseasecondition is identified. In the empirical study described below, twodistinctly different types of image features were developed. One type ofimage feature relates to identifying a photometric and/or morphometricdifference between healthy cells and diseased cells. One technique inidentifying such an image feature is to label carcinoma cells with afluorescent label, and compare images of fluorescently labeled carcinomacells with images of healthy cells, to identify a plurality ofphotometric and morphometric image features associated with thecarcinoma cells. As will be described in greater detail below, suchimage features include differences in the average nucleus size betweenhealthy cells and carcinoma cells, and differences in fluorescent imagesof healthy cells and carcinoma cells. These differences can bequantified based on processing the image data for the population ofcells, to identify images that are more likely to be images of carcinomacells, and to identify images that are more likely to be images ofhealthy cells.

Another type of image feature relates to identifying some differencebetween subpopulations present in a cellular population absent thedisease condition, and subpopulations present in a cellular populationduring the disease condition. For example, CLL is a disease conditionwhere the number of lymphocytes in blood increases relative to thenumbers of other blood cell types. Thus, a change in the ratio oflymphocytes to other blood cell types can be indicative of a diseasecondition.

Once a photometric and/or morphometric image feature associated with thedisease condition is identified, image data are collected from a secondpopulation of cells, in which it is not known whether the diseasecondition exists or not. In a block 408 image data are collected for thesecond population of cells, and then the image data are analyzed for thepresence of the previously identified image feature, to determinewhether the disease condition is present in the second population ofcells.

Significantly, where the imaging systems described above are used tocollect the image data from a population of cells, the image data can becollected quite rapidly. In general, the analysis (i.e., analyzing thecollected image data to either initially identify an image feature or todetermine the presence of a previously identified image feature in apopulation of cells) will be performed off-line, i.e., after thecollection of the image data. Current implementations of imagingprocessing software are capable of analyzing a relatively largepopulation of cells (i.e., tens of thousands of cells) within tens ofminutes using readily available personal computers. However, it shouldbe recognized that as more powerful computing systems are developed andbecome readily available, it may become possible to analyze the imagedata in real-time. Thus, off-line processing of the image data isintended to be exemplary, rather than limiting, and it is contemplatedthat real-time processing of the image data is an alternative.

Where the image feature relates to some photometric and/or morphometricdifference between a healthy cell and a diseased cell, before using animaging instrument to collect image data on the first population ofcells (the population known to be associated with the diseasecondition), it can be desirable to label either the diseased cells orthe healthy cells, particularly where the first population includes amixture of both diseased and healthy cells. This approach facilitatesseparating the collected image data into images corresponding todiseased cells and images corresponding to healthy cells, to facilitateidentification of photometric and/or morphometric image features thatcan be used to distinguish the two. It should be recognized however,that the first population could include only diseased cells, and that ifthe image data of the first population is compared with image data of acell population known to include only healthy cells, the photometricand/or morphometric image features that can be used to distinguish thediseased cells from the healthy cells can readily be identified.

Where the image feature relates to some photometric and/or morphometricdifference between subpopulations present in a cellular populationabsent the disease condition, and subpopulations present in a cellularpopulation associated with disease condition, image data correspondingto the subpopulations present in a healthy cellular population must beprovided before the image data corresponding to the first population ofcells (the population known to be associated with the disease condition)can be analyzed to identify some photometric and/or morphometricdifference between the subpopulations present in the healthy cellularpopulation, and the subpopulations present in the cellular populationhaving the disease condition.

While not strictly required, in a working embodiment of the techniquesdescribed herein, additional processing was implemented to reducecrosstalk and spatial resolution for the multi-channel imaging. Thecrosstalk reduction processing implemented is described in commonlyassigned U.S. Pat. No. 6,763,149, the specification, disclosure and thedrawings of which are hereby specifically incorporated herein byreference as background material. Those of ordinary skill in the artwill recognize that other types of crosstalk reduction techniques couldalternatively be implemented.

Identification of Exemplary Photometric and Morphometric DiseaseCondition Features

In the context of the present disclosure, the multi-spectral imagingflow cytometer described above employs UV excitation capabilities andalgorithms to quantitate DNA content and nuclear morphology, for thepurpose of detecting and monitoring disease conditions, such as chroniclymphocytic leukemia. In addition to employing a flow imaging instrumentincluding a 360 nm UV laser and an optical system optimized fordiffraction-limited imaging performance in the 400-460 nm (DAPIemission) spectral band, an imaging processing system is employed toprocess the image data. A personal computer executing image processingsoftware represents an exemplary imaging processing system. The imagingprocessing software incorporates algorithms enabling photometric and/ormorphometric properties of cells to be determined based on images of thecells. Exemplary algorithms include masking algorithms, algorithms thatdefine nuclear morphology, algorithms for the quantization of cell cyclehistograms, algorithms for analyzing DNA content, algorithms foranalyzing heterochromaticity, algorithms for analyzing N/C ratio,algorithms for analyzing granularity, algorithms for analyzing CD45expression, and algorithms for analyzing other parameters. In addition,the imaging processing software incorporates an algorithm referred to asa classifier, a software based analysis tool that is configured toevaluate a sample population of cells to determine if any diseasecondition image features are present. For determining the presence ofcancer cells, the classifier will analyze the images of the samplepopulation for images having photometric and/or morphometric propertiescorresponding to previously identified photometric and/or morphometricproperties associated with cancer cells.

For samples of cell populations being analyzed to detect CLL, theclassifier will analyze the images of the sample population to separatethe images into different cellular subpopulations (based on differenttypes of blood cells), and determine if the ratios of the subpopulationsindicates the presence of CLL (for example, because of a higher thannormal amount of lymphocytes). Preferably, the classifier configured todetect CLL will separate blood cells into the following subpopulations:lymphocytes, monocytes, basophils, neutrophils, and eosinophils. Theclassifier configured to detect CLL will be based on empirical data fromhealthy patients and from patients with CLL. Classifier profiles for CLLcan be improved by collecting and comparing classifier data for avariety of patients with the same diagnosis. Preferably, large (10,000to 20,000-cell) data sets from each patient will be collected to assessthe existence and diagnostic significance of CLL cell subpopulations forclassifier optimization. Such an optimized classifier can then be usedto monitor patient treatment response and assess residual disease aftertreatment.

Significantly, for detection of epithelial cell carcinomas, high ratesof data acquisition is required. Such cells have been reported to rangefrom 1 cell in 100,000 peripheral blood leukocytes to 1 cell in1,000,000 peripheral blood leukocytes. The ImageStream™ cytometer andIDEAS™ analytical software package discussed above are ideally suitedfor this application. Imagery from peripheral blood leukocytes can beobtained in the absence of artifacts typical of preparing blood films.Large cell numbers (in the tens and hundreds of thousands) can beaccumulated per sample, providing greater confidence in the analysis ofsubpopulations. Immunofluorescent staining with accepted markers (CD5,CDI9, etc.) can easily be correlated with morphology. The quantitativecell classifiers eliminate the subjectivity of human evaluation, givingcomparisons between patients a degree of confidence previouslyunattainable. Longitudinal studies will also benefit greatly by thequantitative analysis, and the ability to digitally store and retrievelarge numbers of cellular image files, particularly as compared to priorart techniques for the retrieval of microscope slides and/or digitalphotographs of relatively small numbers of cells.

Discrimination of Morphological Features Using Fluorescence-BasedMethodologies

A technology employed in detection of cancer cells in a bodily fluidbased on image data of a population of cells from the bodily fluid wasthe development of preliminary absorbance and fluorescence stainingprotocols for simultaneous morphological analysis of bright field andfluorescence imagery.

Initially, investigations considered the simultaneous use of chromogenicstains and fluorescent dyes. The ability of the imaging system discussedabove to produce bright field imagery, as well as multiple colors offluorescence imagery of each cell, raised the possibility ofsimultaneously employing both traditional chromogenic stains andfluorescent dyes for analysis. However, because chromogenic stains donot normally penetrate cell membranes of viable cells, and because theoptical systems discussed above are able to collect laser side scatterimagery, it was determined that much of the information on cellgranularity that was traditionally acquired via stains, such as Eosin,could be obtained using laser side scatter imagery, without the need forcell staining. Numerous cell-permeant fluorescent dyes offer nuclearmorphology without the need for fixing and chromogenic staining. Basedon these considerations, it was determined that fluorescence-basedalternatives for discrimination of morphological image features providea better approach than traditional staining methodologies.

The primary fluorescence-based alternatives to chromogenic stains usefulin conjunction with the optical systems discussed above are fluorescentDNA binding dyes. A wide variety of such dyes are excitable at 488 nm,including several SYTO dyes (Molecular Probes), DRAQ5 (BioStatus),7-AAD, Propidium Iodide (PI), and others. These dyes are alternatives tochromogenic nuclear stains such as Toluidine Blue, Methyl Green, CrystalViolet, Nuclear Fast Red, Carmalum, Celestine Blue, and Hematoxylin. Afluorescent DNA binding dye is generally included in assay protocolsdeveloped for use with the optical systems described above, for thepurposes of defining the shape and boundaries of the nucleus, its area,its texture (analogous to heterochromaticity), as well as to provide DNAcontent information.

IDEAS™, the software image analysis program discussed above, enablesevaluation of combinations of image features from different images ofthe same cell, in order to expand the utility of the fluorescencenuclear image. For example, the nuclear image mask can be subtractedfrom the bright field image mask (which covers the entire cell) as ameans for generating a mask that includes only the cytoplasmic region.Once defined, the cytoplasmic mask can be used to calculate thecytoplasmic area, the N/C ratio, the relative fluorescence intensity ofprobes in the cytoplasm and nucleus, etc., via an intuitive “FeatureManager.” An example of a Feature Manager session for the definition ofthe N/C ratio is shown in FIG. 4. Basic image features associated withany cell image are selected from a list and combined algebraically usinga simple expression builder.

Measurement of Photometric and Morphometric Parameters

In an exemplary implementation of the concepts disclosed herein,ImageStream™ data analysis and cell classification are performedpost-acquisition using the IDEAS™ software package. An annotated IDEAS™software screen capture of an analysis of human peripheral blood isshown in FIG. 5. The IDEAS™ software enables the visualization andphotometric/morphometric analysis of data files containing imagery fromtens of thousands of cells, thereby combining quantitative imageanalysis with the statistical power of flow cytometry.

The exemplary screen shot of FIG. 5 includes images and quantitativedata from 20,000 human peripheral blood mononuclear cells. Whole bloodwas treated with an erythrocyte lysing agent, and the cells were labeledwith an anti-CD45-PerCP mAb (red) and a DNA binding dye (green). Eachcell was imaged in fluorescence using the FL1 and FL4 spectral bands, aswell as dark field and bright field. Images of a plurality of cells in adark field channel 51 a, a green fluorescent channel 51 b, a brightfield channel 51 c, and a red fluorescent channel 51 d can readily beidentified in this Figure. Such a thumbnail image gallery (in the upperleft of the interface) enables the “list mode” inspection of anypopulation of cells. Cell imagery can be pseudo-colored and superimposedfor visualization in the image gallery or enlarged, as shown at thebottom of the interface, for four different cell types (eosinophils 53a, NK cells 53 b, monocytes 53 c, and neutrophils 53 d).

The software also enables one- and two-dimensional plotting of imagefeatures calculated from the imagery. Dots 55 that represent cells inthe two-dimensional plots can be “clicked” to view the associatedimagery in the gallery. The reverse is true as well. Cell imagery can beselected to highlight the corresponding dot in every plot in which thatcell appears. In addition, gates 57 can be drawn on the plots to definesubpopulations, which can then be inspected in the gallery using a“virtual cell sort” functionality. Any image feature calculated from theimagery or defined by the user (i.e., selected from a list of basic andautomatically combined algebraically using a simple expression builder)can be plotted. A dot plot 59 a (displayed at the center left of FIG. 5)shows the clustering resulting from an analysis of CD45 expression(x-axis) versus a dark field granularity metric (y-axis), which issimilar to side-scatter intensity measured in conventional flowcytometry. Plot 59 a reveals lymphocytes (green in a full color image),monocytes (red in a full color image), neutrophils (turquoise in a fullcolor image), and eosinophils (orange in a full color image). A dot plot59 b (displayed at the center right of FIG. 5) substitutes a nucleartexture parameter, “nuclear frequency” for CD45 expression on thex-axis, revealing a putative NK cell population (purple in a full colorimage). Back-displaying the purple population on the left dot plotreveals that this population has the same mean CD45 expression as thelymphocyte population (green on a full color image). The frequencyparameter is one member of the morphologic and photometric image featureset that was developed and incorporated into the IDEAS™ softwarepackage. Table 1 below provides an exemplary listing of exemplaryphotometric and morphometric definitions that can be identified forevery image (or subpopulation, as appropriate). It should be recognizedthat FIG. 5 has been modified to facilitate its reproduction. As afull-color image, the background of each frame including a cell isblack, and the background for each dot plot is black, to facilitatevisualization of the cells and data.

TABLE 1 Morphometric and Photometric Definitions Image FeaturesDescription of Parameters for Each Image (6 per object) Area Area ofmask in pixels Aspect Ratio Aspect ratio of mask Aspect Ratio IntensityIntensity-weighted aspect ratio of mask Background Mean Intensity Meanintensity of pixels outside of mask Background StdDev Intensity Standarddeviation of intensity of pixels outside of mask Centroid X Centroid ofmask in horizontal axis Centmid X Intensity Intensity-weighted centroidof mask in horizontal axis Centroid Y Centroid of mask in vertical axisCentmid Y Intensity Intensity-weighted centroid of mask in vertical axisCombined Mask Intensity Total intensity of image using logical “OR” ofall six image masks Frequency Variance of intensity of pixels withinmask Gradient Max Maximum intensity gradient of pixels within maskGradient RMS RMS of intensity gradient of pixels within mask IntensityBackground-corrected sum of pixel intensities within mask Major AxisMajor axis of mask in pixels Major Axis Intensity Intensity-weightedmajor axis of mask in pixels Mean Intensity Total Intensity of imagedivided by area of mask Minimum Intensity Minimum pixel intensity withinmask Minor Axis Minor axis of mask in pixels Minor Axis IntensityIntensity-weighted minor axis of mask in pixels Object Rotation AngleAngle of major axis relative to axis of flow Object Rotation AngleIntensity Angle of intensity-weighted major axis relative to axis offlow Peak Intensity Maximum pixel intensity within mask Perimeter Numberof edge pixels in mask Spot Large Max Maximum pixel intensity withinlarge bright spots Spot Large Total Sum of pixel intensities withinlarge bright spots Spot Medium Max Maximum pixel intensity withinmedium-sized bright spots Spot Medium Total Sum of pixel intensitieswithin medium-sized bright spots Spot Raw Max Un-normalized maximumpixel intensity within large bright spots Spot Raw Total Sum ofun-normalized pixel intensities within large bright spots Spot Small MaxMaximum pixel intensity within small bright spots Spot Small Total Sumof pixel intensities within small bright spots Total Intensity Sum ofpixel intensities within mask Spot Count Number of spots detected inimage Combined Mask Area Area of logical ‘OR” of all six image masks inpixels Flow Speed Camera line readout rate in Hertz at time object wasimaged Object Number Unique object number Similarity Pixel intensitycorrelation between two images of the same object User-Defined FeaturesAny algebraic combination of imagery and masks User-Defined Masks Erode,dilate, threshold, Boolean combinations User-Defined Populations AnyBoolean combination of defined populations

Image features that quantitate morphology are shown in italics inTable 1. Each image feature is automatically calculated for all sixtypes of images (dark field, bright field, and four fluorescent images,that are simultaneously captured) for each cell, when an image data setis loaded into the software.

Over 35 image features are calculated per image, which amounts to over200 image features per cell in assays that employ all six images, notincluding user-defined image features. Each cell is also assigned aunique serial number and time stamp, enabling kinetic studies over cellpopulations.

Selection of a Photometric/Morphometric Image Features for CarcinomaCells

It was initially proposed that bladder epithelial cells would be used toinvestigate morphometric differences between normal and epithelialcarcinoma cells. However, the initial samples of bladder washings thatwere analyzed revealed that the cell number per sample was highlyvariable, and generally too low to be practical for use in theImageStream™ instrument. Mammary epithelial cells were therefore used inplace of bladder cells. Mammary cells were chosen because normal,primary cells of this kind are commercially available(Clonetics/InVitrogen) and will expand as adherent cells in short-termtissue culture with specialized growth media. In addition, mammaryepithelial carcinoma cells derived from breast cancer metastases areavailable from the American Type Tissue Culture Collection (ATCC). Inorder to better control for tumor to tumor variability, three differentmammary epithelial carcinoma cell lines were studied: HCC-1 500, HCC-1569, and HCC-1428. These lines were established from metastases in threeseparate patients and were purchased from ATCC as frozen stocks. Thecell lines grew adherent to plastic, were expanded by routine tissueculture methods, and used experimentally.

Normal and cancerous mammary epithelial cells were harvested separatelyby brief incubation with trypsin/EDTA at 37 degrees Celsius. The cellswere washed once in cold phosphate buffer solution (PBS) containing 1%FCS, counted, and used experimentally. The three separate mammaryepithelial carcinoma cell lines were pooled in equal proportions for theexperiments described below.

Normal mammary epithelial cells were stained with afluorescein-conjugated monoclonal antibody to the HLA Class I MHC cellsurface protein by incubating the cells with the appropriate,predetermined dilution of the mAb for 30 minutes at 4 degrees C. Despitethe fact that mammary carcinomas are known to down-regulate Class I MHCexpression, as a precaution, the normal cells were fixed in 1%paraformaldehyde to limit passive transfer to the carcinoma cells. Thecombined mammary carcinoma cells lines were also fixed in 1%paraformaldehyde and added to the normal mammary cell population. DRAQ5(BioStatus, Ltd, Leicestershire, UK), a DNA binding dye that can beexcited with a 488 nm laser and emits in the red waveband, was added tothe sample prior to running on the ImageStream™ instrument. The labelingof normal mammary epithelial cells with anti-Class I MHC mAb enabled thenormal cells to be identified in mixes of normal and carcinoma cells,thereby providing an objective “truth” to facilitate the identificationof image features distinguishing normal epithelial cell from epithelialcarcinoma cells.

Normal peripheral blood was obtained from AlICells (San Diego, Calif.).Whole blood was incubated with FITC conjugated anti-CD45 mAb, which isexpressed at some level on all peripheral white blood cells. Red bloodcells were then lysed by incubation of the whole blood in a BectonDickinson FACSLyse™ for 3 minutes at room temperature. The cells werewashed in PBS, counted and fixed with 1% paraformaldehyde. Mammaryepithelial carcinoma cells were prepared as above, fixed in 1%paraformaldehyde and added to the peripheral blood cells. DRAQ5 was thenadded as a nuclear stain, and the cells were run on the ImageStream™instrument.

Image files containing image data of the cell mixes described above(normal mammary epithelial cells mixed with mammary carcinoma cells, andnormal peripheral blood cells mixed with mammary carcinoma cells) wereanalyzed using the IDEAS™ software package with the results describedbelow.

After performing spectral compensation on the data file, an initialvisual inspection was performed to compare normal mammary epithelialcells (positive for anti-HLA-FITC) to the carcinoma cells (unstained foranti-HLA-FITC). Representative images of normal cells are shown in FIG.6, while representative images of carcinoma cells are shown in FIG. 7.In each Figure, each horizontal row includes four simultaneouslyacquired images of a single cell. Images in columns 61 a and 71 acorrespond to blue laser side scatter images (i.e., dark field images),images in columns 61 b and 71 b correspond to green HLA-FITCfluorescence images, images in columns 61 c and 71 c correspond tobright field images, and images in columns 61 d and 71 d correspond tored nuclear fluorescence. As described above, the preferred imagingsystem is capable of simultaneously collecting six different types ofimages of a single cell (a dark field image, a bright field image, andfour fluorescence images); in FIGS. 6 and 7, two of the fluorescencechannels have not been utilized. It should be recognized that FIGS. 6and 7 have been modified to facilitate their reproduction. As full-colorimages, the backgrounds of FIGS. 6 and 7 are black, images in columns 61a and 71 a are blue, images in columns 61 b and 71 b are green, imagesin columns 61 c and 71 c are grayscale images on a gray background, andimages in columns 61 d and 71 d are red.

When visually comparing full-color images of FIGS. 6 and 7, it isimmediately apparent that images of normal mammary epithelial cells incolumn 61 c (the green fluorescence channel) of FIG. 6 are vivid, whileimages of carcinoma cells in column 71 c (the green fluorescencechannel) of FIG. 7 can hardly be distinguished. It is also apparent thatwhile none of the dark field images (columns 61 a and 71 a) areparticularly intense, the dark field images (column 61 a) of normalmammary epithelial cells in FIG. 6 are significantly more intense thanare the dark field images (column 71 a) of carcinoma cells in FIG. 7.Yet another qualitative observation that can be readily made is that theaverage intensity of the red fluorescence images (column 71 d) ofcarcinoma cells in FIG. 7 is substantially greater than the averageintensity of the red fluorescence images (column 61 a) in FIG. 6.Further qualitative observations indicate that normal cells have higherheterogeneity, were generally larger, and had lower nuclear intensity.The subsequent analysis sought to quantitate these differences, as wellas to discover additional parameters that might have discriminationcapability. A screen capture of the corresponding IDEAS™ analysis isshown in FIG. 8A.

The analysis shown in FIG. 8A proceeded from a dot plot 81 in FIG. 8B.Single cells were first identified, based on dot plot 81, which wasdefined as bright field area versus aspect ratio. A gate (not separatelyshown) was drawn around the population containing putative single cellsbased on the criteria of the area being sufficiently large to excludedebris, and the aspect ratio being greater than −0.5, which eliminatesdoublets and clusters of cells. The veracity of the gating was tested byexamining random cells both within and outside of the gate using theclick-on-a-dot visualization functionality.

Next, the normal mammary cells were distinguished from the mammarycarcinoma cells using the anti-HLA-FITC marker that was applied only tothe normal cells. A solid yellow histogram 85 a of FITC intensity wasgenerated and is shown in FIG. 8C. A gate 83 was then drawn around theFITC positive (normal mammary epithelial cells) and FITC negative(mammary epithelial carcinoma cells), resulting in a subpopulation of2031 normal cells, and a subpopulation of 611 carcinoma cells. Thesesubpopulations were then used to identify image features thatquantitatively discriminated between normal and cancerous cells, basedon differential histograms. It should be recognized that FIG. 8A hasbeen modified to facilitate its reproduction. As a full-color image, thebackground of each frame including a cell is black, and the backgroundfor each dot plot and histogram is black, to facilitate visualization ofthe cells and data. This modification resulted in the even distributionof dots 81 a, even though such an even distribution was not present inthe full color image.

The remaining ten histograms (i.e., histograms 85 b-85 k) shown in FIGS.8D-8M are differential histograms of the normal cells 87 a (shown asgreen in a full-color image) and carcinoma cells 87 b (shown as red in afull-color image), with each histogram representing a differentquantitative image feature. The ten discriminating image features fellinto five distinct classes: scatter intensity, scatter texture,morphology, nuclear intensity, and nuclear texture. Differentialhistograms 85 b, 85 c, and 85 d demonstrate the difference between thetwo populations using three different, but correlated, scatter intensityimage features: “scatter mean intensity” (total intensity divided bycell area), “scatter intensity” (total intensity minus background), and“scatter spot small total” (total intensity of local maxima). Althoughall three scatter intensity image features provided good discrimination,“scatter mean intensity” (histogram 85 b) was the most selective.

Differential histograms 85 e and 85 f quantitated scatter texture usingeither an intensity profile gradient metric (“scatter gradient RMS”;histogram 85 e) or the variance of pixel intensities (“scatterfrequency”; histogram 85 f), which proved more selective.

Differential histograms 85 g, 85 h and 85 i plotted the cellular area(bright field area, histogram 85 g), nuclear area (from the DNAfluorescence imagery, histogram 85 h), and cytoplasmic area(cellular/nuclear area, histogram 85 i). The carcinoma cell lines weregenerally smaller in bright field area, confirming the qualitativeobservations from cell imagery. While the nuclear area of the carcinomacell lines was proportionately smaller than that of the normal cells(e.g., the Nuclear/Cellular area ratio was not discriminatory), thecytoplasmic area was significantly lower in the carcinoma cells.

Finally, differential histograms 85 j and 85 k plotted the nuclear meanintensity (histogram 85 j) and nuclear frequency (heterochromaticity,histogram 85 k), respectively. As in the case of scatter, both of theseimage features provided some discriminatory power.

The multispectral/multimodal imagery collected by the ImageStream™instrument and analyzed using the IDEAS™ software package in thisengineered experiment revealed a number of significant differences indark field scatter, morphology, and nuclear staining between normalepithelial and epithelial carcinoma cells. While it is well-recognizedthat cells adapted to tissue culture have undergone a selection processthat may have altered their cellular characteristics, these datademonstrate that it is feasible to build an automated classifier thatuses the morphometric and photometric image features identified anddescribed above to separate normal from transformed epithelial cells,and possibly other cell types.

A further experimental investigation analyzed image data collected froma mixture of normal peripheral blood cells and mammary carcinoma cells.As shown in FIG. 5 (discussed above), cell classification of humanperipheral blood can be achieved using a flow imaging system configuredto simultaneously obtain a plurality of images of each cell, and usingan automatic image analysis program (with the ImageStream™ instrumentrepresenting an exemplary imaging system, and the IDEAS™ softwarepackage representing an exemplary image analysis program). Using CD45expression combined with an analysis of dark field light scatterproperties, cells can be separated into five distinct populations basedon the image data collected by the flow imaging system: lymphocytes,monocytes, neutrophils, eosinophils and basophils This separation ofhuman peripheral blood into distinct subpopulations is shown in greaterdetail in FIG. 9, which includes exemplary relative abundance data forthe different subpopulations. The veracity of the classifications wasdetermined by using population-specific monoclonal antibody markers andbackgating marker-positive cells on the scatter vs. CD45 plot, as wellas morphological analysis of the associated imagery. The x-axis of thegraph in FIG. 9 corresponds to anti-CD45-FITC Intensity, while they-axis corresponds to dark field scatter intensity.

In order to determine whether the techniques disclosed herein (utilizingthe flow imaging instrument system described above, which is exemplifiedby the ImageStream™ instrument, and imaging analysis software, which isexemplified by the IDEAS™ software package) could discriminateepithelial carcinoma cells from normal PBMC, an artificial mixture oftumor cells and normal PBMC was produced as described above. The cellmixture was labeled with an anti-CD45-FITC mAb and a fluorescent DNAbinding dye in order to differentiate PBMC subpopulations, generally asdescribed above. A comparison of the scatter vs. CD45 bivariate plotsfor normal peripheral blood mononuclear cells and the PBMC sample spikedwith the carcinoma cells is shown in FIGS. 10A and 10B. FIG. 10Agraphically illustrates a distribution of normal peripheral bloodmononuclear cells (PBMC) based on image data collected from a populationof cells that does not include mammary carcinoma cells. FIG. 10Bgraphically illustrates a distribution of normal PBMC and mammarycarcinoma cells based on image data collected from a population of cellsthat includes both cell types, illustrating how the distribution of themammary carcinoma cells is distinguishable from the distribution of thenormal PBMC cells. In this analysis, carcinoma cells 101 a fall welloutside of a normally defined PBMC population 101 b, as confirmed byvisual inspection of the outlier population.

As shown in FIGS. 11A and 11B, carcinoma cells 111 a can also bediscriminated from normal PBMC 111 b using some of the morphometric andphotometric image features identified in FIG. 8A (e.g., nuclear area,cytoplasmic area, scatter intensity, and scatter frequency). FIG. 11Agraphically illustrates a distribution of normal PBMC and mammarycarcinoma cells based on measured cytoplasmic area derived from imagedata collected from a population of cells that includes both cell types,illustrating how the distribution of cytoplasmic area of mammarycarcinoma cells is distinguishable from the distribution of cytoplasmicarea of the normal PBMC cells. FIG. 11B graphically illustrates adistribution of normal PBMC and mammary carcinoma cells based onmeasured scatter frequency derived from image data collected from apopulation of cells that includes both cell types, illustrating how thedistribution of the scatter frequency of the mammary carcinoma cells isdistinguishable from the distribution of the scatter frequency of thenormal PBMC cells. Although these image features were initiallyidentified for the purpose of discriminating between normal mammary andmammary carcinoma cells, they provide a high level of discriminationbetween mammary epithelial carcinoma cells and PBMC. Significantly,normal epithelial cells would be even more clearly differentiated fromPBMC and distinct from the epithelial carcinoma cells using theseparameters.

It should be recognized that FIGS. 10A, 10B, 11A, and 11B have beenmodified to facilitate their reproduction. As a full-color images, thebackground of each frame including a dot plot is black, to facilitatevisualization of the cells and/or data, and dots representing PBMC cellsand carcinoma cells are different colors.

The results noted above were verified by visual inspection of thesegregated images (i.e., the images separated into subpopulationscorresponding to carcinoma cells and healthy cells using one or more ofthe above identified photometric and/or morphometric parameters). Imagegallery data were produced from the spiked PBMC data described above.FIG. 12 includes representative images from the carcinoma cellpopulation, obtained using an overlay composite of bright field andDRAQ5 DNA fluorescence (red, with the image processing being performedby the image analysis software). FIG. 13 includes images of the fiveperipheral blood mononuclear cell populations defined using dark fieldscatter, CD45 (green), and DRAQ5 (red) for nuclear morphology. Note thatthe two Figures are at different size scales. It should be recognizedthat FIG. 12 has been modified to facilitate its reproduction. As afull-color image, the background of FIG. 12 is black, the background ofeach frame including a cell is brown/grey, and the nucleus of each iscell is red. FIG. 13 has been similarly modified to facilitate itsreproduction. As a full-color image, the background of FIG. 13 is black,the periphery of each cell is green, and the nucleus of each is cell isred.

Significantly, the above studies demonstrate the feasibility ofoptically discriminating a subpopulation of normal epithelial cells froma subpopulation of transformed cells by analyzingmulti-spectral/multimodal image data from a mixed population of suchcells, where the image data are simultaneously collected. The abovestudies also demonstrate the feasibility of detecting epithelialcarcinoma cells in blood by analyzing multi-spectral/multimodal imagedata from a mixed population of such cells, where the image data aresimultaneously collected.

With respect to applying the concepts described herein to a specificdisease condition concept, because of the relatively high operatingspeed of the exemplary imaging system (˜100 cells/second or ˜350,000cells/hour), and because of the relatively large amount of imageinformation collected for each cell (high resolution bright field image,dark field image, and four fluorescence images), it is believed that theconcept disclosed herein is particularly suitable for the detection andmonitoring of chronic lymphocytic leukemia.

In such an application, a 360 nm UV laser will be incorporated into thesimultaneous multispectral/multimodal imaging system, and the optics ofthe imaging system will be optimized for diffraction-limited imagingperformance in the 400-460 nm (DAPI emission) spectral band. Theexemplary imaging system used in the empirical studies detailed above(i.e., the ImageStream™ instrument) employs a solid state, 200 mW, 488nm laser for fluorescence excitation. While such a laser wavelengthexcites a broad range of fluorochromes, it is not optimal for cell cycleanalysis due to its inability to excite DAPI, which bindsstoichiometrically to DNA. In addition, the beam is configured to have anarrow width, which improves overall sensitivity in exchange forincreased measurement variation from cell to cell. Feasibility studiesemploying propidium iodide as a DNA stain indicate that the imagingsystem employing the 488 nm laser can generate cell cycle histogramshaving G0/G1 peak coefficients of variation of ˜5%.

In order to generate high resolution cell cycle histograms for thedetection of changes in DNA content associated with CLL, the DAPIoptimized 360 nm UV laser will instead be used. The beam will beconfigured to have a relatively wide illumination cross-section (˜100microns), so that under typical operating conditions, DAPI excitationconsistency will be within 1% from cell to cell. Overall, cell cyclehistogram CV is expected to be about 2-3%. In addition, the optics inthe exemplary instrument used in the empirical studies discussed aboveare diffraction-limited from 460-750 nm, which does not cover the DAPIspectral emission band. Thus, such optics will be replaced with opticsthat are configured to achieve diffraction-limited imaging performancein the 400-460 nm spectral band, in order to measure detailed nuclearcharacteristics of diagnostic value, such as notched morphology andheterochromaticity.

Particularly for use with applying the concept disclosed herein for thedetection of changes in DNA content associated with CLL, it would bedesirable to provide image processing software incorporating additionalmasking algorithms and image features that define nuclear morphology innormal samples, beyond those described above.

The morphometric image feature set available in the exemplary imageprocessing software discussed above does not include boundary contourimage features that quantitate nuclear lobicity, number ofinvaginations, and similar parameters. Because such image featurescapture many of the qualitative observations of nuclear morphologytraditionally used by hematopathologists, they would be of extremelyhigh utility in the analysis of leukocytes. Incorporation of suchalgorithms and image features would enable improved automatedclassification of normal cells, precursors, and transformed cells.

The boundary contour masking algorithm and associated image featuresemployed in the empirical studies discussed above improve cellclassification between eosinophils, neutrophils, monocytes, basophils,and lymphocytes in about ⅓ of cells of each type, as a function of theirorientation with respect to the imaging plane. Cells that are not in oneof two preferred orientations (out of six possible orientations) do notbenefit from the previously employed algorithm and image features. Toimprove the cell classification, the boundary contour algorithm andimage features can be extended to consistently classify normalleukocytes, independent of their rotational orientation, which will leadto a first-pass classifier between normal and transformed cells, byincreasing the statistical resolution between the expected locations ofnormal cell distributions, thereby improving the ability to flagabnormal cells that fall outside the expected positions. Such aclassifier will also enable the image features to be characterized forthe morphologic differences observed between normal and transformedlymphocytes, to further improve discrimination, using the techniquesgenerally discussed above.

To configure the imaging analysis software for the detection of changesin DNA content associated with CLL, an automated classifier will beincorporated into the software package. The automated classifier willincorporate at least one or more of the following photometric and/ormorphometric parameters: DNA content, nuclear morphology,heterochromaticity, N/C ratio, granularity, CD45 expression, and otherparameters. As discussed above, the classifier will be configured toanalyze image data corresponding to a population of blood cells, toclassify the population into the following subpopulations: lymphocytes,monocytes, basophils, neutrophils, and eosinophils.

Automated differential analysis of PBMC based on multimodal imagerysimultaneously collected from cells in flow will be performed usingimaging systems consistent with those described above, and imagingprocessing software consistent with those described above. PBMC will bestained with FITC conjugated anti-CD45 and the DNA binding dye, DAPI.Peripheral blood leukocytes will be classified in a five-partdifferential analysis into lymphocytes, monocytes, basophils,neutrophils, and eosinophils, generally as indicated in FIGS. 5, 9, and13.

Data sets from peripheral blood leukocytes from CLL patients will beacquired and analyzed, as discussed above. The classification schemedeveloped for normal peripheral blood leukocytes will be applied tothese data sets, and the identification of CLL cells will be determinedby comparison with normal profiles. Various classifiers will beevaluated to determine which segments CLL cells best exemplify,generally as described above with respect to the histograms of FIG. 8.Among these will be: cell size, nuclear size, nuclear to cytoplasmicratio, nuclear contour, nuclear texture, and cytoplasmic granules.Results will be compared with standard blood films from CLL patientsamples to determine the veracity of the technique.

In addition to the normal staining protocol utilizing anti-CD45 as amarker, peripheral blood leukocytes will be stained with monoclonalantibodies to CD5 and CD2O, plus DAPI, before image data are collected.This approach will enable the identification of the CLL cells accordingto accepted flow cytometric criteria. In this way, morphologic criteriacan be correlated with the immunophenotype.

Analyzing large (10,000 to 20,000 white blood cell) data sets frommultiple CLL patients will facilitate the optimization and selection ofphotometric and morphometric image features that can be used classifyblood cells by subpopulation (i.e., lymphocytes, monocytes, basophils,neutrophils, and eosinophils).

Morphological heterogeneity has been observed in CLL cells; however, anaccurate objective appreciation of the degree of this has not beenachieved due to the technical difficulty of preparing and assessingperipheral blood films from patients consistently. Acquisition of largedata sets from CLL patients using the multimodal imaging systemsdiscussed above will enable the objective analysis of the degree ofmorphological heterogeneity by the imaging processing software package.The classifier(s) developed above will be applied to these data sets,and morphological heterogeneity assessed by analyzing the degree towhich the particular classifier (e.g., nuclear size, N/C ratio, etc.)applies across the large populations of CLL cells. Based on thisanalysis, the classifier that most accurately identifies the greatestpercentage of CLL cells will be optimized, so that the entire populationis included by the classifier.

As noted above, when applied to CLL, the techniques disclosed herein arenot being used to separate a population of cells into a subpopulationcorresponding to healthy cells, and a subpopulation corresponding todiseased cells. Instead, image data collected from a population of bloodcells will be used to separate the population of blood cells intosubpopulations based on blood cell type (i.e., lymphocytes, monocytes,basophils, neutrophils, and eosinophils) Because CLL is associated withan increase in the amount of lymphocytes present in the blood cellpopulation (i.e., an increase in the lymphocytes subpopulation),detecting an increase in lymphocytes provides an indication of theexistence of the disease condition (i.e., CLL). While the preferredmethod described herein involves separating the blood cell populationinto a plurality of different subpopulations, it should be recognizedthat a CLL detection technique could be implemented simply by separatingthe blood cell population into a lymphocyte subpopulation and anon-lymphocyte subpopulation. Using empirical data representing averagelymphocyte subpopulations in healthy patients, detection of ahigher-than-average lymphocyte subpopulation provides an indication of aCLL disease condition.

In addition to initially detecting the CLL disease condition, theimaging and analysis techniques discussed in detail above can be appliedto follow patients with CLL longitudinally to determine their responseto treatment, stability of the clinical response, and disease relapse.Changes in peripheral blood populations, including both normal and anyresidual CLL, can be followed and correlated with clinical outcome.

Exemplary Computing Environment

As noted above, an aspect of the present invention involves imageanalysis of a plurality of images simultaneously collected from membersof the population of cells. Reference has been made to an exemplaryimage analysis software package. FIG. 14 and the following relateddiscussion are intended to provide a brief, general description of asuitable computing environment for practicing the present invention,where the image processing required is implemented using a computingdevice generally like that shown in FIG. 14. Those skilled in the artwill appreciate that the required image processing may be implemented bymany different types of computing devices, including a laptop and othertypes of portable computers, multiprocessor systems, networkedcomputers, mainframe computers, hand-held computers, personal dataassistants (PDAs), and on other types of computing devices that includea processor and a memory for storing machine instructions, which whenimplemented by the processor, result in the execution of a plurality offunctions.

An exemplary computing system 150 suitable for implementing the imageprocessing required in the present invention includes a processing unit154 that is functionally coupled to an input device 152, and an outputdevice 162, e.g., a display. Processing unit 154 include a centralprocessing unit (CPU 158) that executes machine instructions comprisingan image processing/image analysis program for implementing thefunctions of the present invention (analyzing a plurality of imagessimultaneously collected for members of a population of objects toenable at least one characteristic exhibited by members of thepopulation to be measured). In at least one embodiment, the machineinstructions implement functions generally consistent with thosedescribed above, with reference to the flowchart of FIG. 3, as well asthe exemplary screenshots. Those of ordinary skill in the art willrecognize that processors or central processing units (CPUs) suitablefor this purpose are available from Intel Corporation, AMD Corporation,Motorola Corporation, and from other sources.

Also included in processing unit 154 are a random access memory 156(RAM) and non-volatile memory 160, which typically includes read onlymemory (ROM) and some form of memory storage, such as a hard drive,optical drive, etc. These memory devices are bi-directionally coupled toCPU 158. Such storage devices are well known in the art. Machineinstructions and data are temporarily loaded into RAM 156 fromnon-volatile memory 160. Also stored in memory are the operating systemsoftware and ancillary software. While not separately shown, it shouldbe understood that a power supply is required to provide the electricalpower needed to energize computing system 150.

Input device 152 can be any device or mechanism that facilitates inputinto the operating environment, including, but not limited to, a mouse,a keyboard, a microphone, a modem, a pointing device, or other inputdevices. While not specifically shown in FIG. 14, it should beunderstood that computing system 150 is logically coupled to an imagingsystem such as that schematically illustrated in FIG. 1, so that theimage data collected are available to computing system 150 to achievethe desired image processing. Of course, rather than logically couplingthe computing system directly to the imaging system, data collected bythe imaging system can simply be transferred to the computing system bymeans of many different data transfer devices, such as portable memorymedia, or via a network (wired or wireless). Output device 162 will mosttypically comprise a monitor or computer display designed for humanvisual perception of an output image.

Comparison of Two Cell Populations to Evaluate Patient Health

As discussed in detail above, image data for a plurality of images ofindividual cells that are acquired simultaneously can be used to detecta disease condition. Note that such an application of the presentapproach is based on identifying and/or quantifying differences betweena first cell population and a second cell population, by analyzing theimage data collected for each cell population. Generally, as describedabove, the image data can be analyzed to identify quantifiablephotometric and morphometric differences between the first and secondcell populations. The image data can also be used to identify a celltype present in one of the first and second cell populations, but not inthe other of the first and second cell populations. Similarly, the imagedata can also be used to identify differences in the relative numbers ofcell types in the first and second cell populations, to determine ifthere are more or less of a particular cell type in the first populationof cells, as compared to the second population of cells (and viceversa). These techniques can provide diagnostic information about apatient from whom the cells are obtained, beyond simply determining if aspecific disease condition exists.

For example, assume a patient provides a first sample of blood or bodilyfluid taken on a first date, and image data from that first populationof cells are generated as described above. Image data from a subsequentsample (i.e., a second population of cells) taken on a later date can becompared to the image data from the first population of cells toidentify differences between those populations. it may be possible tocorrelate those differences to some phenomenon occurring between thecollection of the first population of cells and the second population ofcells. By way of example, such phenomena can include, but are notlimited to, exposure to stress conditions (such an analysis will enableresearchers to better understand cellular reactions to specific stressfactors, such as heat, cold, exercise, mental stress, emotional stress,etc.), exposure to radiation (such an analysis will enable researchersto better understand cellular reactions to specific types of radiation),a change in diet (such an analysis will enable researchers to betterunderstand cellular reactions to specific types of dietary changes), achange in lifestyle (such an analysis will enable researchers to betterunderstand cellular reactions to specific types of lifestyle changes), achange in a patient's use of nutritional supplements (such an analysiswill enable researchers to better understand cellular reactions to theuse of specific nutritional supplements), types of dietary changes, anda change in a patient's use of medications (such an analysis will enableresearchers to better understand cellular reactions to the use ofspecific medications). While some such phenomena may be related to aspecific disease condition, other phenomena may more generally berelated to a patient's health and/or well being.

In the example provided immediately above, the first and secondpopulation of cells were obtained from a person at different times. Itshould also be understood that the first and second population of cellscan be obtained from a person at the same time, but then treateddifferently before being imaged as described above. For example, asingle blood sample or bodily fluid sample can be acquired from aperson, and that sample can be split into two fractions, one for thefirst population and the other fraction for the second population. Imagedata for the first fraction (the first population of cells) can then beacquired. The second fraction (the second population of cells) can beexposed to one of more phenomena, such as those noted above, and thenimaged (note that the second fraction can be manipulated and/or exposedto some stimulus other than those specifically identified above). Theimage data from the first and second populations of cells can then beanalyzed to determine how the cell populations differ as a result ofchanges in the second population caused by the phenomena, manipulation,or stimulation.

FIG. 15 is a flow chart 401 schematically illustrating exemplary stepsthat can be used to analyze two populations of cells based on images ofthe cell populations, in order to identify and/or quantify differencesbetween the cell populations. In a particularly preferred exemplaryembodiment, each cell population is obtained from a bodily fluid, suchas blood. In a block 403, an imaging system, such as the exemplaryimaging system described in detail above, is used to collect image datafrom a first population of biological cells. In a block 405, the imagingsystem is also used to collect image data from a second population ofbiological cells. As noted above, the first and second cell populationscan be acquired at different times, or may be obtained from a singlesample acquired at one time and the sample then split into twofractions. In a block 407, the image data from the two cell populationsare computationally analyzed (using a processor, such as provided by acomputing device or an application specific circuit, or other logicdevice) to identify differences between the cell populations. In atleast some exemplary embodiments, the differences are quantified interms of at least one photometric or morphometric image feature. Interms of the exemplary IDEAS™ software discussed above, the analysis caneither look for specific changes identified by a user (such as a changein the relative abundance of cell types between the two populations), orthe analysis can look for any and all differences that are identifiablebased on the image data, and then rank those differences (in terms ofmorphological image features, photometric image features, and cellabundance) in order of their significance. Generally, as discussedabove, the use of one or more fluorescent labels can facilitate thecomparative analysis.

The high level steps of FIG. 15 can be used for many different types ofinvestigations. The following provides a brief description of sixdifferent investigations that can be performed consistent with theexemplary steps of FIG. 15.

A first investigation analyzes image data from a first population ofcells and a second population of cells to determine if any variation ina specific cell type present in both populations is indicative of adisease condition. This technique is described in significant detailabove in the specification, in the context of using the first populationto identify disease related image features, and looking for such imagefeatures in the second population.

A second investigation analyzes image data from a first population ofcells and a second population of cells to determine if any variationexists for a specific cell type present in both populations, regardlessof whether the difference is indicative of a disease condition. Thistechnique is generally directed at acquiring the first and second cellpopulations from a person at different times, and determining if thereis any difference between the same cell type in the first and secondpopulations. If data are available regarding conditions experienced bythe person during the time between acquiring the samples, then anattempt can be made to correlate the changes to such conditions. Evenwhere no correlation can be found, any change identified may beindicative of the health of the person. For example, some cellularchanges may suggest that the health of the patient has improved ordeclined, even if no specific disease condition is identified.Furthermore, even if no change in the first and second cell populationsis identified, that finding may itself comprise valuable diagnosticdata, either indicating that the health of the person has notappreciably changed, or if the person's health has changed, indicatingthat the specific cell type is likely not related to the change inhealth.

A third investigation analyzes image data from a first population ofcells and a second population of cells to determine if there has been achange in the relative distributions of different types of cells presentin both populations, where such a change can be indicative of a diseasecondition. This analysis will include determining if a specific celltype is present in the first cell population but not the second cellpopulation, and vice versa, as well as determining how the relativepercentage of cell types present in both the first and second cellpopulations has changed. This technique is described in significantdetail above in the specification, in the context of using relative cellabundance to determine if a disease condition is indicated.

A fourth investigation also analyzes image data from a first populationof cells and a second population of cells to determine if there has beena change in the relative abundance of different types of cells presentin both populations, where such a change is not limited to indicating aspecific disease condition, but may still be relevant to the health ofthe person from whom the first and second cell populations wereobtained. Again, this analysis includes determining if a specific celltype is present in the first cell population but not the second cellpopulation, and vice versa, as well as determining how the relativepercentage of cell types present in both the first and second cellpopulations has changed. This technique is generally directed atacquiring the first and second cell populations from a person atdifferent times, and determining if there are differences between thedistributions of different cell types in the first and secondpopulations. If data are available indicating conditions experienced bythe person during the time between acquiring the samples, then anattempt can be made to correlate the changes to such conditions. Evenwhere no correlation can be found, any changes identified may beindicative of the health of the person. For example, some cellulardistribution changes may suggest that the health of the patient hasimproved or declined, even if no specific disease condition isidentified. Furthermore, even if no change in the cellular distributionsin the first and second cell populations is identified, that fact itselfmay comprise valuable diagnostic data, either indicating that the healthof the person has not appreciably changed, or if the person's health haschanged, indicating that the cellular distribution is likely not relatedto the change in health.

A fifth investigation analyzes image data from a first population ofcells and a second population of cells to determine how the secondpopulation of cells responds to a stimulus not applied to the firstpopulation of cells, in order to detect a disease condition. In general,this technique is based on acquiring one sample from a person, andsplitting that sample into two different fractions (the two differentcell populations could be acquired from the person at different times;however, doing so will introduce an additional variable). The firstpopulation of cells serves as a control. A stimulus is applied to thesecond population of cells. The term “stimulus” should be broadlyinterpreted as something likely to induce a change in the secondpopulation of cells relative to the first population of cells. By way ofexample, such a stimulus can include, but is not limited to, exposingthe second population to a change in temperature, exposing the secondpopulation to a reagent, exposing the second population to radiation,exposing the second population to a change in environmental conditions,and exposing the second population to a drug. In general, the firstpopulation of cells will not be exposed to the stimulus. It should benoted that the first population of cells may be manipulated in somefashion to enable changes between the population of cells to be morereadily apparent, such as labeling the first population of cells.

A sixth investigation, similar to the fifth investigation discussedabove, analyzes image data from a first population of cells and a secondpopulation of cells to determine how the second population of cellsresponds to a stimulus not applied to the first population of cells. Thesixth investigation differs from the fifth in that a change detected maynot be indicative of a specific disease condition, while still beingrelevant to the health of the person from whom the populations of cellswere obtained. Furthermore, as generally discussed above, even where thecomparison of the first population to the second population does notindicate any significant changes, that information may in itself berelevant to the health of the person.

Although the concepts disclosed herein have been described in connectionwith the preferred form of practicing them and modifications thereto,those of ordinary skill in the art will understand that many othermodifications can be made thereto within the scope of the claims thatfollow. Accordingly, it is not intended that the scope of these conceptsin any way be limited by the above description, but instead bedetermined entirely by reference to the claims that follow.

The invention in which an exclusive right is claimed is defined by thefollowing:
 1. A method for detecting at least one difference between afirst and a second population of cells using image data collectedseparately for each population, where the image data include a pluralityof images of individual cells that are acquired simultaneously, for thefirst population and for the second population, comprising the steps of:(a) imaging the first population of cells and the second population ofcells to collect the image data, such that a plurality of images for thecells are simultaneously collected for cells in the first population andfor cells in the second population, the plurality of images comprisingat least one of the following two types of images: (i) multispectralimages; and (ii) multimodal images; and (b) analyzing the image datacollected to identify at least one difference between the image datacollected for the first and the second population of cells.
 2. Themethod of claim 1, further comprising the step of determining whetherthe difference between the first and second population of cells isindicative of a health of a person from which the first and secondpopulation of cells were obtained.
 3. The method of claim 1, wherein thestep of analyzing the image data collected to identify at least onedifference between the first and the second population of cellscomprises the step of identifying at least one morphometric imagefeature that differs between the first and the second population ofcells.
 4. The method of claim 1, wherein the step of analyzing the imagedata collected to identify at least one difference between the first andsecond population of cells comprises the step of identifying at leastone photometric image feature that differs between the first and thesecond population of cells.
 5. The method of claim 1, wherein the stepof analyzing the image data collected to identify at least onedifference between the first and second population of cells comprisesthe step of identifying at least one cell type present in one of thefirst and second population of cells, but not in the other of the firstand the second population of cells.
 6. The method of claim 1, whereinthe step of analyzing the image data collected to identify at least onedifference between the first and the second population of cellscomprises the step of quantifying a difference between a distribution ofcell types in the first and the second population of cells.
 7. Themethod of claim 1, wherein the step of analyzing the image datacollected to identify at least one difference between the first and thesecond population of cells comprises the step of quantifying adifference between a first type of cell present in the first populationof cells and the same type of cell present in the second population ofcells.
 8. The method of claim 1, further comprising the step of adding areagent to at least one of the first and the second population of cellsbefore imaging that population of cells, wherein the reagent comprisesat least one reagent selected from the group consisting of: (a) a labelthat facilitates identification of one or more cellular biomolecules;and (b) a stimulus likely to induce a change in the population of cellsexposed to the stimulus.
 9. The method of claim 1, wherein the step ofimaging the first and the second population of cells to collect theimage data comprises the step of simultaneously collecting at least twotypes of images for a cell, selected from a group consisting of thefollowing types of images: a bright field image, a dark field image, anda fluorescence image.
 10. The method of claim 1, further comprising thestep of acquiring the first population of cells from a person at a firsttime, and acquiring the second population of cells from the person at alater time.
 11. The method of claim 1, further comprising the step ofexposing the second population of cells to a stimulus without exposingthe first population of cells to the same stimulus, before collectingthe image data for the second population of cells.
 12. The method ofclaim 1, wherein the difference quantified between the first and secondpopulation of cells is a distribution of a molecule within the cells ofeach sample.
 13. A method for detecting at least one difference betweena first and a second population of cells using image data collectedseparately for each population, where the image data include a pluralityof images of individual cells that are acquired simultaneously, for thefirst population and for the second population, comprising the steps of:(a) acquiring the first population of cells and the second population ofcells from a person at the same time; (b) imaging the first populationof cells and the second population of cells to collect the image data,such that a plurality of images for the cells are simultaneouslycollected for cells in the first population and for cells in the secondpopulation, the plurality of images comprising at least one of thefollowing two types of images: (i) multispectral images; and (ii)multimodal images; and (c) analyzing the image data collected toidentify at least one difference between the image data collected forthe first and the second population of cells.
 14. A method for detectingat least one difference between a first and a second population of cellsusing image data collected separately for each population, where theimage data include a plurality of images of individual cells that areacquired simultaneously, for the first population and for the secondpopulation, comprising the steps of: (a) exposing the second populationof cells to a stimulus without exposing the first population of cells tothe same stimulus (b) after exposing the second population of cells tothe stimulus, imaging the first population of cells and the secondpopulation of cells to collect the image data, such that a plurality ofimages for the cells are simultaneously collected for cells in the firstpopulation and for cells in the second population, the plurality ofimages comprising at least one of the following two types of images: (i)multispectral images; and (ii) multimodal images; and (c) analyzing theimage data collected to identify at least one difference between theimage data collected for the first and the second population of cells.